Finish Activity 10
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{
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"cell_type": "code",
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"cell_type": "markdown",
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"execution_count": null,
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"id": "1dccb5e3-1cd3-4732-a7ba-81a8a94c89dc",
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"id": "526d98f6-5a18-4ed9-9870-29b08b54e073",
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"source": [
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"source": []
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"As you can see from the previous examples, a complete simulation may require several multiple scattering calculations, for instance to calculate the total intensity of a substrate or to optimize the geometry of a system. As the calculations are often time consuming, it can be useful to distribute these tasks over several processors to make the most of hardware resources.\n",
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"Although MsSpec is not fully parallelized, the code does offer a number of features, which we will explore here.\n",
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"\n",
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"## Matrix inversion parallelization\n",
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"\n",
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"When available during installation, MsSpec will link with the system lapack library. It will be used to invert the matrix in the `inversion` option of the MsSpec `calculator`. To allow MsSpec to use this shared memory parallelism, you need to set the number of cores to be used in the `OMP_NUM_THREADS` environment variable.\n",
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"\n",
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"You can set this variable just for the execution of your script. For example:\n",
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"\n",
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"```sh\n",
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"$ OMP_NUM_THREADS=12 python my_script.py\n",
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"```\n",
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"\n",
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"will use 12 cores for inverting the matrix in your script.\n",
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"\n",
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"It is also possible to set environment variable inside your python script.\n",
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"\n",
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"```python\n",
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"import os\n",
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"\n",
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"os.environ['OMP_NUM_THREADS'] = 12\n",
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"```\n",
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"\n",
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"It may be useful for technical reasons or to use different number of cores in some parts of your script.\n",
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"\n",
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"## Process-based parallelism\n",
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"\n",
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"Another kind of parallelization used in MsSpec is multiprocessing. Quite often, you need to run different *independent* calculations. MsSpec provides a simple *looper* that can be useful for multiprocessing. Let's demonstrate it with the previous example CO/Fe(001).\n",
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"\n",
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"[This script](COFe_mp.py) is the multiprocessed version of the previous one. You can see that the previous nested for loops are now replaced by some declarative content (lines 63-67) and the definition of a `process` function (whose name \n",
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"can be changed).\n",
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"\n",
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"With the `msspec.looper` package, the user define `Sweep` objects that are parameters of the calculation or of the cluster. The `process` function must accept as many arguments as parameters to sweep (+ the `**kwargs`).\n",
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"\n",
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"A `Looper` object is created (line 76) and the `process` function is set to its `pipeline` attribute (line 77). When MsSpec will run the `looper`, it will combine all parameters values to unique individual sets and MsSpec will distribute the calculations over the number of processors specified in the `ncpu` option.\n",
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"\n",
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":::{literalinclude} COFe_mp.py\n",
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":lineno-start: 63\n",
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":linenos: true\n",
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":lines: 63-84\n",
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":::\n",
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"\n",
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"\n",
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"::::{tab-set}\n",
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"\n",
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":::{tab-item} <i class=\"fa-solid fa-circle-question\"></i> Quiz\n",
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"In the paper discussed in {ref}`RFactor`, experimental values of the anisotropy suggest an adsorption height between 0.2 and 0.6 Å. Modify the script to add another sweep for variying the adsorption height of the CO molecule.\n",
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":::\n",
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"\n",
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"::::"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "9da716db-069f-422b-a5fd-2c2b509eb621",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Å\n"
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]
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}
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],
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"source": [
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"print('\\U0000212B')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0a1fabce-c42d-4cb4-9720-bcd21ff0cd09",
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"metadata": {},
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"source": [
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"```{toggle}\n",
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"\n",
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":::{literalinclude} COFe_mp_completed.py\n",
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":lineno-start: 63\n",
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":linenos: true\n",
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":lines: 63-86\n",
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":emphasize-lines: 6,7, 9,10, 18\n",
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":::\n",
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"\n",
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"\n",
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"```"
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]
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}
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}
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],
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],
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"metadata": {
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"metadata": {
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@ -33,7 +118,7 @@
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"name": "python",
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"name": "python",
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"nbconvert_exporter": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"pygments_lexer": "ipython3",
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"version": "3.11.13"
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"version": "3.11.3"
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}
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}
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},
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},
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"nbformat": 4,
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"nbformat": 4,
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from ase import Atoms
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from ase.build import add_adsorbate, bulk
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from msspec.calculator import MSSPEC, RFACTOR
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from msspec.utils import hemispherical_cluster
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from msspec.looper import Sweep, Looper
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import numpy as np
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def create_cluster(height=1., theta=45, phi=0, bond_length=1.15):
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# Fill the body of this function. The 'cluster' object in built according to
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# values provided by the keyword arguments:
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# height (in angströms): the 'z' distance between the Fe surface and the C atom
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# theta and phi (in degrees): the polar and azimuthal orientation of the CP molecule
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# (theta=0° aligns the molecule withe the surface normal
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# phi=0° corresponds to the [100] direction of iron)
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# bond_length (in angströms): the C-O distance
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iron = bulk('Fe', cubic=True)
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cluster = hemispherical_cluster(iron, diameter=5, planes=2, emitter_plane=1)
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t = np.radians(theta)
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p = np.radians(phi)
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z = bond_length * np.cos(t)
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x = bond_length * np.sin(t) * np.cos(p)
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y = bond_length * np.sin(t) * np.sin(p)
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CO=Atoms('CO',positions=[(0,0,0),(x,y,z)])
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add_adsorbate(cluster,CO, height=height)
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# Keep those 2 lines at the end of your function
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# Store some information in the cluster object
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cluster.info.update(adsorbate={'theta': theta, 'phi': phi, 'height': height,
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'bond_length': bond_length})
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return cluster
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def compute_polar_scan(cluster, folder='calc'):
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calc = MSSPEC(spectroscopy='PED', algorithm='expansion', folder=folder)
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calc.set_atoms(cluster)
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# SSC calculations
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calc.calculation_parameters.scattering_order = 1
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# Add temperature effects
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[atom.set('mean_square_vibration', 0.005) for atom in cluster]
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calc.calculation_parameters.vibrational_damping = 'averaged_tl'
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polar_angles = np.arange(-5, 85, 0.5)
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# set the Carbon as absorber and compute the polar scan
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cluster.absorber = cluster.get_chemical_symbols().index('C')
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data = calc.get_theta_scan(level='1s', theta=polar_angles, kinetic_energy=1202)
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calc.shutdown()
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return data
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###############################################################################
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# Main part
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###############################################################################
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# 1) Multiprocess calculations
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theta = Sweep(key='theta', comments="The molecule tilt angle",
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start=50, stop=60, step=1, unit='degree')
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phi = Sweep(key='phi', comments="The molecule azimuthal angle",
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values=[0,45], unit='degree')
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def process(theta, phi, **kwargs):
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cluster = create_cluster(theta=theta, phi=phi, height=0.6, bond_length=1.157)
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i = kwargs.get('sweep_index')
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data = compute_polar_scan(cluster, folder=f'calc_{i:d}')
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dset = data[-1]
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return dset.theta, dset.cross_section
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looper = Looper()
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looper.pipeline = process
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df = looper.run(theta, phi, ncpu=4)
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# Black magic to convert the pandas dataframe object 'df' to the
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# parameters dict and the resulst list (will be easier in a future
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# version ;-) ).
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parameters = df.to_dict('list')
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results = np.reshape(parameters.pop('output'), (df.shape[0]*2,-1))
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# 2) R-Factor analysis
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# Load the experimental data
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exp_data = np.loadtxt('experimental_data.txt')
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# Create an R-Factor calculator
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rfc = RFACTOR()
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rfc.set_references(exp_data[:,0], exp_data[:,1])
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# Perform the R-Factor analysis
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data = rfc.run(*results, **parameters)
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data.view()
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@ -0,0 +1,98 @@
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from ase import Atoms
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from ase.build import add_adsorbate, bulk
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from msspec.calculator import MSSPEC, RFACTOR
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from msspec.utils import hemispherical_cluster
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from msspec.looper import Sweep, Looper
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import numpy as np
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def create_cluster(height=1., theta=45, phi=0, bond_length=1.15):
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# Fill the body of this function. The 'cluster' object in built according to
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# values provided by the keyword arguments:
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# height (in angströms): the 'z' distance between the Fe surface and the C atom
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# theta and phi (in degrees): the polar and azimuthal orientation of the CP molecule
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# (theta=0° aligns the molecule withe the surface normal
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# phi=0° corresponds to the [100] direction of iron)
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# bond_length (in angströms): the C-O distance
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iron = bulk('Fe', cubic=True)
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cluster = hemispherical_cluster(iron, diameter=5, planes=2, emitter_plane=1)
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t = np.radians(theta)
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p = np.radians(phi)
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z = bond_length * np.cos(t)
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x = bond_length * np.sin(t) * np.cos(p)
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y = bond_length * np.sin(t) * np.sin(p)
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CO=Atoms('CO',positions=[(0,0,0),(x,y,z)])
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add_adsorbate(cluster,CO, height=height)
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# Keep those 2 lines at the end of your function
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# Store some information in the cluster object
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cluster.info.update(adsorbate={'theta': theta, 'phi': phi, 'height': height,
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'bond_length': bond_length})
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return cluster
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def compute_polar_scan(cluster, folder='calc'):
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calc = MSSPEC(spectroscopy='PED', algorithm='expansion', folder=folder)
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calc.set_atoms(cluster)
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# SSC calculations
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calc.calculation_parameters.scattering_order = 1
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# Add temperature effects
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[atom.set('mean_square_vibration', 0.005) for atom in cluster]
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calc.calculation_parameters.vibrational_damping = 'averaged_tl'
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polar_angles = np.arange(-5, 85, 0.5)
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# set the Carbon as absorber and compute the polar scan
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cluster.absorber = cluster.get_chemical_symbols().index('C')
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data = calc.get_theta_scan(level='1s', theta=polar_angles, kinetic_energy=1202)
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calc.shutdown()
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return data
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###############################################################################
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# Main part
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###############################################################################
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# 1) Multiprocess calculations
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theta = Sweep(key='theta', comments="The molecule tilt angle",
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start=50, stop=60, step=1, unit='degree')
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phi = Sweep(key='phi', comments="The molecule azimuthal angle",
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values=[0,45], unit='degree')
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height = Sweep(key='height', comments="The molecule adsorption height",
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start=0.2, stop=0.6, num=3, unit='angström')
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def process(theta, phi, height, **kwargs):
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cluster = create_cluster(theta=theta, phi=phi, height=height, bond_length=1.157)
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i = kwargs.get('sweep_index')
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data = compute_polar_scan(cluster, folder=f'calc_{i:d}')
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dset = data[-1]
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return dset.theta, dset.cross_section
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looper = Looper()
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looper.pipeline = process
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df = looper.run(theta, phi, height, ncpu=4)
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# Black magic to convert the pandas dataframe object 'df' to the
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# parameters dict and the resulst list (will be easier in a future
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# version ;-) ).
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parameters = df.to_dict('list')
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results = np.reshape(parameters.pop('output'), (df.shape[0]*2,-1))
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# 2) R-Factor analysis
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# Load the experimental data
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exp_data = np.loadtxt('experimental_data.txt')
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# Create an R-Factor calculator
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rfc = RFACTOR()
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rfc.set_references(exp_data[:,0], exp_data[:,1])
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# Perform the R-Factor analysis
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data = rfc.run(*results, **parameters)
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data.view()
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@ -0,0 +1,250 @@
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# Polar angle (°) C1s/O1s Signal (a.u.)
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-5.163000000000000256e+00 3.649999999999999911e-01
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-4.858999999999990216e+00 3.639999999999999902e-01
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-4.556000000000000050e+00 3.629999999999999893e-01
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-4.251999999999999780e+00 3.619999999999999885e-01
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-3.947999999999990184e+00 3.609999999999999876e-01
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-3.677999999999999936e+00 3.599999999999999867e-01
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-3.375000000000000000e+00 3.579999999999999849e-01
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-3.104999999999999982e+00 3.569999999999999840e-01
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-2.766999999999999904e+00 3.559999999999999831e-01
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-2.462999999999989864e+00 3.549999999999999822e-01
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-2.193000000000000060e+00 3.539999999999999813e-01
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-1.991000000000000103e+00 3.529999999999999805e-01
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-1.754999999999999893e+00 3.519999999999999796e-01
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-1.519000000000009898e+00 3.509999999999999787e-01
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-1.215000000000000080e+00 3.499999999999999778e-01
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-1.012000000000000011e+00 3.499999999999999778e-01
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-6.749999999999970468e-01 3.499999999999999778e-01
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-4.389999999999930069e-01 3.509999999999999787e-01
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-1.009999999999989934e-01 3.519999999999999796e-01
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2.019999999999979867e-01 3.529999999999999805e-01
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5.739999999999979563e-01 3.529999999999999805e-01
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9.789999999999989821e-01 3.539999999999999813e-01
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1.417000000000000037e+00 3.539999999999999813e-01
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1.956999999999990081e+00 3.539999999999999813e-01
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2.327999999999999847e+00 3.549999999999999822e-01
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2.564999999999999947e+00 3.559999999999999831e-01
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2.766999999999999904e+00 3.579999999999999849e-01
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2.970000000000000195e+00 3.589999999999999858e-01
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3.071000000000000174e+00 3.609999999999999876e-01
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3.239999999999989999e+00 3.619999999999999885e-01
|
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|
3.407999999999999918e+00 3.629999999999999893e-01
|
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|
3.576999999999999957e+00 3.649999999999999911e-01
|
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|
3.712000000000000188e+00 3.659999999999999920e-01
|
||||||
|
3.813000000000000167e+00 3.679999999999999938e-01
|
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|
3.880999999999999783e+00 3.679999999999999938e-01
|
||||||
|
3.947999999999990184e+00 3.699999999999999956e-01
|
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|
4.352999999999989988e+00 3.699999999999999956e-01
|
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|
4.758000000000000007e+00 3.709999999999999964e-01
|
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|
5.197000000000000064e+00 3.709999999999999964e-01
|
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|
5.602000000000000313e+00 3.719999999999999973e-01
|
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|
5.972999999999999865e+00 3.719999999999999973e-01
|
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|
6.243000000000000327e+00 3.729999999999999982e-01
|
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|
6.479000000000000092e+00 3.739999999999999991e-01
|
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|
6.682000000000000384e+00 3.750000000000000000e-01
|
||||||
|
6.884000000000000341e+00 3.760000000000000009e-01
|
||||||
|
6.985000000000000320e+00 3.770000000000000018e-01
|
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|
7.187999999999999723e+00 3.780000000000000027e-01
|
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|
7.322999999999989740e+00 3.790000000000000036e-01
|
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|
7.389999999999999680e+00 3.810000000000000053e-01
|
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|
7.491999999999999993e+00 3.820000000000000062e-01
|
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|
7.727999999999989988e+00 3.830000000000000071e-01
|
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|
8.064999999999999503e+00 3.830000000000000071e-01
|
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|
8.403000000000009351e+00 3.840000000000000080e-01
|
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|
8.808000000000010488e+00 3.840000000000000080e-01
|
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|
9.212999999999990308e+00 3.850000000000000089e-01
|
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|
9.618000000000000327e+00 3.840000000000000080e-01
|
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|
9.921000000000010033e+00 3.830000000000000071e-01
|
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|
1.025900000000000034e+01 3.830000000000000071e-01
|
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1.063000000000000078e+01 3.820000000000000062e-01
|
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|
1.103500000000000014e+01 3.810000000000000053e-01
|
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|
1.143999999999999950e+01 3.800000000000000044e-01
|
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|
1.174399999999999977e+01 3.800000000000000044e-01
|
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|
1.208099999999999952e+01 3.790000000000000036e-01
|
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1.245199999999999996e+01 3.790000000000000036e-01
|
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|
1.289100000000000001e+01 3.790000000000000036e-01
|
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|
1.316099999999999959e+01 3.800000000000000044e-01
|
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|
1.343099999999999916e+01 3.810000000000000053e-01
|
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|
1.366699999999999982e+01 3.820000000000000062e-01
|
||||||
|
1.400399999999999956e+01 3.830000000000000071e-01
|
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|
1.430799999999999983e+01 3.850000000000000089e-01
|
||||||
|
1.454400000000000048e+01 3.860000000000000098e-01
|
||||||
|
1.471299999999999919e+01 3.870000000000000107e-01
|
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|
1.508399999999999963e+01 3.870000000000000107e-01
|
||||||
|
1.562400000000000055e+01 3.870000000000000107e-01
|
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|
1.612999999999999901e+01 3.880000000000000115e-01
|
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|
1.663700000000000045e+01 3.880000000000000115e-01
|
||||||
|
1.704200000000000159e+01 3.880000000000000115e-01
|
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|
1.744699999999999918e+01 3.880000000000000115e-01
|
||||||
|
1.791900000000000048e+01 3.890000000000000124e-01
|
||||||
|
1.825600000000000023e+01 3.900000000000000133e-01
|
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|
1.855999999999999872e+01 3.900000000000000133e-01
|
||||||
|
1.889799999999999969e+01 3.920000000000000151e-01
|
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|
1.910000000000000142e+01 3.930000000000000160e-01
|
||||||
|
1.933599999999999852e+01 3.930000000000000160e-01
|
||||||
|
1.953900000000000148e+01 3.950000000000000178e-01
|
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|
1.987600000000000122e+01 3.960000000000000187e-01
|
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|
2.011199999999999832e+01 3.950000000000000178e-01
|
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|
2.048400000000000176e+01 3.940000000000000169e-01
|
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|
2.078699999999999903e+01 3.930000000000000160e-01
|
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|
2.112500000000000000e+01 3.920000000000000151e-01
|
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|
2.149599999999999866e+01 3.910000000000000142e-01
|
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|
2.190099999999999980e+01 3.900000000000000133e-01
|
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|
2.227199999999999847e+01 3.890000000000000124e-01
|
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|
2.264300000000000068e+01 3.880000000000000115e-01
|
||||||
|
2.314999999999999858e+01 3.870000000000000107e-01
|
||||||
|
2.362199999999999989e+01 3.860000000000000098e-01
|
||||||
|
2.409400000000000119e+01 3.850000000000000089e-01
|
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|
2.460099999999999909e+01 3.850000000000000089e-01
|
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|
2.520799999999999841e+01 3.840000000000000080e-01
|
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|
2.578200000000000003e+01 3.830000000000000071e-01
|
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|
2.632199999999999918e+01 3.820000000000000062e-01
|
||||||
|
2.692899999999999849e+01 3.820000000000000062e-01
|
||||||
|
2.746900000000000119e+01 3.810000000000000053e-01
|
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|
2.810999999999999943e+01 3.800000000000000044e-01
|
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|
2.871799999999999997e+01 3.800000000000000044e-01
|
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|
2.922400000000000020e+01 3.790000000000000036e-01
|
||||||
|
2.973000000000000043e+01 3.790000000000000036e-01
|
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|
3.016900000000000048e+01 3.780000000000000027e-01
|
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|
3.067500000000000071e+01 3.770000000000000018e-01
|
||||||
|
3.128200000000000003e+01 3.770000000000000018e-01
|
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|
3.155199999999999960e+01 3.780000000000000027e-01
|
||||||
|
3.195700000000000074e+01 3.800000000000000044e-01
|
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|
3.219299999999999784e+01 3.820000000000000062e-01
|
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|
3.246300000000000097e+01 3.840000000000000080e-01
|
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|
3.280100000000000193e+01 3.850000000000000089e-01
|
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|
3.324000000000000199e+01 3.870000000000000107e-01
|
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|
3.354299999999999926e+01 3.890000000000000124e-01
|
||||||
|
3.388100000000000023e+01 3.900000000000000133e-01
|
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|
3.431900000000000261e+01 3.920000000000000151e-01
|
||||||
|
3.469100000000000250e+01 3.940000000000000169e-01
|
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|
3.502799999999999869e+01 3.960000000000000187e-01
|
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|
3.546699999999999875e+01 3.970000000000000195e-01
|
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|
3.580400000000000205e+01 3.990000000000000213e-01
|
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|
3.610799999999999699e+01 4.010000000000000231e-01
|
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|
3.637800000000000011e+01 4.030000000000000249e-01
|
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|
3.661399999999999721e+01 4.040000000000000258e-01
|
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|
3.691799999999999926e+01 4.060000000000000275e-01
|
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|
3.722200000000000131e+01 4.079999999999999738e-01
|
||||||
|
3.742399999999999949e+01 4.099999999999999756e-01
|
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|
3.762700000000000244e+01 4.109999999999999765e-01
|
||||||
|
3.779500000000000171e+01 4.119999999999999774e-01
|
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|
3.803099999999999881e+01 4.139999999999999791e-01
|
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|
3.830100000000000193e+01 4.149999999999999800e-01
|
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|
3.850399999999999778e+01 4.159999999999999809e-01
|
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|
3.867300000000000182e+01 4.179999999999999827e-01
|
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|
3.890899999999999892e+01 4.189999999999999836e-01
|
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|
3.921300000000000097e+01 4.199999999999999845e-01
|
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|
3.938100000000000023e+01 4.209999999999999853e-01
|
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|
3.965100000000000335e+01 4.229999999999999871e-01
|
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|
3.988799999999999812e+01 4.239999999999999880e-01
|
||||||
|
4.009000000000000341e+01 4.249999999999999889e-01
|
||||||
|
4.039399999999999835e+01 4.259999999999999898e-01
|
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|
4.073100000000000165e+01 4.269999999999999907e-01
|
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|
4.103499999999999659e+01 4.279999999999999916e-01
|
||||||
|
4.130499999999999972e+01 4.299999999999999933e-01
|
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|
4.160900000000000176e+01 4.309999999999999942e-01
|
||||||
|
4.187899999999999778e+01 4.319999999999999951e-01
|
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|
4.221600000000000108e+01 4.329999999999999960e-01
|
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|
4.248599999999999710e+01 4.329999999999999960e-01
|
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|
4.275600000000000023e+01 4.339999999999999969e-01
|
||||||
|
4.295799999999999841e+01 4.349999999999999978e-01
|
||||||
|
4.322800000000000153e+01 4.359999999999999987e-01
|
||||||
|
4.346500000000000341e+01 4.380000000000000004e-01
|
||||||
|
4.376800000000000068e+01 4.390000000000000013e-01
|
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|
4.403799999999999670e+01 4.400000000000000022e-01
|
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|
4.424099999999999966e+01 4.420000000000000040e-01
|
||||||
|
4.444299999999999784e+01 4.430000000000000049e-01
|
||||||
|
4.461200000000000188e+01 4.440000000000000058e-01
|
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|
4.478099999999999881e+01 4.450000000000000067e-01
|
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|
4.494899999999999807e+01 4.460000000000000075e-01
|
||||||
|
4.521900000000000119e+01 4.470000000000000084e-01
|
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|
4.545600000000000307e+01 4.480000000000000093e-01
|
||||||
|
4.582699999999999818e+01 4.480000000000000093e-01
|
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|
4.619800000000000040e+01 4.480000000000000093e-01
|
||||||
|
4.650200000000000244e+01 4.490000000000000102e-01
|
||||||
|
4.683899999999999864e+01 4.490000000000000102e-01
|
||||||
|
4.724399999999999977e+01 4.500000000000000111e-01
|
||||||
|
4.751400000000000290e+01 4.510000000000000120e-01
|
||||||
|
4.775000000000000000e+01 4.510000000000000120e-01
|
||||||
|
4.808800000000000097e+01 4.520000000000000129e-01
|
||||||
|
4.852600000000000335e+01 4.530000000000000138e-01
|
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|
4.886399999999999721e+01 4.540000000000000147e-01
|
||||||
|
4.923499999999999943e+01 4.540000000000000147e-01
|
||||||
|
4.964000000000000057e+01 4.540000000000000147e-01
|
||||||
|
5.001100000000000279e+01 4.530000000000000138e-01
|
||||||
|
5.034899999999999665e+01 4.530000000000000138e-01
|
||||||
|
5.071999999999999886e+01 4.540000000000000147e-01
|
||||||
|
5.102400000000000091e+01 4.540000000000000147e-01
|
||||||
|
5.136099999999999710e+01 4.550000000000000155e-01
|
||||||
|
5.173199999999999932e+01 4.560000000000000164e-01
|
||||||
|
5.210300000000000153e+01 4.570000000000000173e-01
|
||||||
|
5.254200000000000159e+01 4.580000000000000182e-01
|
||||||
|
5.281199999999999761e+01 4.580000000000000182e-01
|
||||||
|
5.314999999999999858e+01 4.580000000000000182e-01
|
||||||
|
5.352100000000000080e+01 4.570000000000000173e-01
|
||||||
|
5.382500000000000284e+01 4.570000000000000173e-01
|
||||||
|
5.422899999999999920e+01 4.570000000000000173e-01
|
||||||
|
5.466799999999999926e+01 4.570000000000000173e-01
|
||||||
|
5.503900000000000148e+01 4.580000000000000182e-01
|
||||||
|
5.541100000000000136e+01 4.580000000000000182e-01
|
||||||
|
5.581600000000000250e+01 4.600000000000000200e-01
|
||||||
|
5.618699999999999761e+01 4.600000000000000200e-01
|
||||||
|
5.655799999999999983e+01 4.600000000000000200e-01
|
||||||
|
5.696300000000000097e+01 4.600000000000000200e-01
|
||||||
|
5.743500000000000227e+01 4.590000000000000191e-01
|
||||||
|
5.794200000000000017e+01 4.590000000000000191e-01
|
||||||
|
5.848100000000000165e+01 4.600000000000000200e-01
|
||||||
|
5.912299999999999756e+01 4.600000000000000200e-01
|
||||||
|
5.946000000000000085e+01 4.590000000000000191e-01
|
||||||
|
5.986500000000000199e+01 4.590000000000000191e-01
|
||||||
|
6.013499999999999801e+01 4.580000000000000182e-01
|
||||||
|
6.040500000000000114e+01 4.560000000000000164e-01
|
||||||
|
6.074199999999999733e+01 4.550000000000000155e-01
|
||||||
|
6.107999999999999829e+01 4.540000000000000147e-01
|
||||||
|
6.138400000000000034e+01 4.530000000000000138e-01
|
||||||
|
6.185600000000000165e+01 4.520000000000000129e-01
|
||||||
|
6.236200000000000188e+01 4.520000000000000129e-01
|
||||||
|
6.283500000000000085e+01 4.520000000000000129e-01
|
||||||
|
6.330700000000000216e+01 4.510000000000000120e-01
|
||||||
|
6.371200000000000330e+01 4.500000000000000111e-01
|
||||||
|
6.398199999999999932e+01 4.490000000000000102e-01
|
||||||
|
6.428600000000000136e+01 4.480000000000000093e-01
|
||||||
|
6.469100000000000250e+01 4.470000000000000084e-01
|
||||||
|
6.496099999999999852e+01 4.450000000000000067e-01
|
||||||
|
6.533199999999999363e+01 4.440000000000000058e-01
|
||||||
|
6.566899999999999693e+01 4.420000000000000040e-01
|
||||||
|
6.604000000000000625e+01 4.410000000000000031e-01
|
||||||
|
6.634399999999999409e+01 4.400000000000000022e-01
|
||||||
|
6.674899999999999523e+01 4.380000000000000004e-01
|
||||||
|
6.712000000000000455e+01 4.369999999999999996e-01
|
||||||
|
6.755899999999999750e+01 4.359999999999999987e-01
|
||||||
|
6.806499999999999773e+01 4.349999999999999978e-01
|
||||||
|
6.857099999999999795e+01 4.339999999999999969e-01
|
||||||
|
6.897599999999999909e+01 4.329999999999999960e-01
|
||||||
|
6.917900000000000205e+01 4.319999999999999951e-01
|
||||||
|
6.958400000000000318e+01 4.299999999999999933e-01
|
||||||
|
6.995499999999999829e+01 4.289999999999999925e-01
|
||||||
|
7.035999999999999943e+01 4.269999999999999907e-01
|
||||||
|
7.076500000000000057e+01 4.249999999999999889e-01
|
||||||
|
7.117000000000000171e+01 4.229999999999999871e-01
|
||||||
|
7.157500000000000284e+01 4.209999999999999853e-01
|
||||||
|
7.194599999999999795e+01 4.189999999999999836e-01
|
||||||
|
7.238500000000000512e+01 4.179999999999999827e-01
|
||||||
|
7.299200000000000443e+01 4.169999999999999818e-01
|
||||||
|
7.370099999999999341e+01 4.169999999999999818e-01
|
||||||
|
7.430800000000000693e+01 4.159999999999999809e-01
|
||||||
|
7.491599999999999682e+01 4.159999999999999809e-01
|
||||||
|
7.545600000000000307e+01 4.139999999999999791e-01
|
||||||
|
7.612999999999999545e+01 4.129999999999999782e-01
|
||||||
|
7.670399999999999352e+01 4.109999999999999765e-01
|
||||||
|
7.717700000000000671e+01 4.099999999999999756e-01
|
||||||
|
7.771699999999999875e+01 4.099999999999999756e-01
|
||||||
|
7.818899999999999295e+01 4.109999999999999765e-01
|
||||||
|
7.886400000000000432e+01 4.129999999999999782e-01
|
||||||
|
7.970799999999999841e+01 4.139999999999999791e-01
|
||||||
|
8.055100000000000193e+01 4.159999999999999809e-01
|
||||||
|
8.139499999999999602e+01 4.179999999999999827e-01
|
||||||
|
8.220499999999999829e+01 4.209999999999999853e-01
|
||||||
|
8.291299999999999670e+01 4.219999999999999862e-01
|
||||||
Loading…
Reference in New Issue