{ "metadata": { "name": "", "signature": "sha256:0b9db22f7b198625c92dd6df4c0f2057b88bde9e1a6cc1fc4f32e9447335a4cf" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "# 2nd High Lift Prediction Workshop \n", "\n", "#### Alpha = 7deg , Re = 15E+06 , DLR - F11 configuration
\n", "\n", "\n", "![2ndHLPW](http://hiliftpw.larc.nasa.gov/Images/f11-piv-lswt-b.jpg) \n", "\n", "\n", "#### References\n", "\n", "http://hiliftpw.larc.nasa.gov/\n", "\n", "\n", " \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define case name\n", "This is the solver case to be analysed" ] }, { "cell_type": "code", "collapsed": false, "input": [ "case_name = 'v2'" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define Data Location\n", "For remote data the interaction will use ssh to securely interact with the data
\n", "This uses the reverse connection capability in paraview so that the paraview server can be submitted to a job scheduler
\n", "Note: The default paraview server connection will use port 11111" ] }, { "cell_type": "code", "collapsed": false, "input": [ "remote_data = True\n", "\n", "data_dir='/gpfs/cfms/workarea/projects/hyper_flux/high_lift/7deg_v3'\n", "\n", "data_host='acimpoeru@vis03'\n", "\n", "remote_server_auto = True\n", "\n", "paraview_cmd='mpiexec /gpfs/cfms/apps/bin/pvserver'\n", "if not remote_server_auto:\n", " paraview_cmd=None\n", "\n", "if not remote_data:\n", " data_host='localhost'\n", " paraview_cmd=None" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Initialise Environment" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline\n", "from paraview.simple import *\n", "paraview.simple._DisableFirstRenderCameraReset()\n", "import pylab as pl\n", "import math\n", "import numpy as np" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Populating the interactive namespace from numpy and matplotlib\n", "paraview version 4.2.0-69-g6666fe8" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data Connection\n", "This starts paraview server on remote host and connects" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from zutil.post import pvserver_connect\n", "if remote_data:\n", " pvserver_connect(data_host=data_host,data_dir=data_dir,paraview_cmd=paraview_cmd)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[acimpoeru@vis03] Executing task 'port_test'\n", "Selected Port: 12000\n", "[acimpoeru@vis03] Executing task 'pvserver'\n", "[acimpoeru@vis03] run: /bin/bash -l -c \"cd /gpfs/cfms/workarea/projects/hyper_flux/high_lift/7deg_v3 && sleep 2;mpiexec /gpfs/cfms/apps/bin/pvserver -rc --client-host=localhost -sp=12000\"\n", "[acimpoeru@vis03] out: \n", "[acimpoeru@vis03] out: \t\t _____ ______ __ __ _____ \n", "[acimpoeru@vis03] out: \t\t / ____| ____| \\/ |/ ____|\n", "[acimpoeru@vis03] out: \t\t | | | |__ | \\ / | (___ \n", "[acimpoeru@vis03] out: \t\t | | | __| | |\\/| |\\___ \\ \n", "[acimpoeru@vis03] out: \t\t | |____| | | | | |____) |\n", "[acimpoeru@vis03] out: \t\t \\_____|_| |_| |_|_____/ \n", "[acimpoeru@vis03] out: \n", "[acimpoeru@vis03] out: \n", "[acimpoeru@vis03] out: \n", "[acimpoeru@vis03] out: ++++++++++++++++++++++++++++: System Data :++++++++++++++++++++++++++++\n", "[acimpoeru@vis03] out: + Hostname = vis03\n", "[acimpoeru@vis03] out: + Kernel = 2.6.32-358.el6.x86_64\n", "[acimpoeru@vis03] out: + RHEL Release = Red Hat Enterprise Linux Server release 6.4 (Santiago)\n", "[acimpoeru@vis03] out: + Uptime = 14:30:00 up 3 days, 4:14, 10 users,\n", "[acimpoeru@vis03] out: + CPU = 2x Intel Xeon X5570 @ 2.93GHz\n", "[acimpoeru@vis03] out: + Memory = 132148768 kB\n", "[acimpoeru@vis03] out: ++++++++++++++++++++++++++++: User Data :++++++++++++++++++++++++++++++\n", "[acimpoeru@vis03] out: + Username = acimpoeru\n", "[acimpoeru@vis03] out: +++++++++++++++++++++++: Contact Information :+++++++++++++++++++++++++\n", "[acimpoeru@vis03] out: + in case of any problems, contact: support@cfms.org.uk\n", "[acimpoeru@vis03] out: + for feedback, contact: feedback@cfms.org.uk \n", "[acimpoeru@vis03] out: +++++++++++++++++++++: Maintenance Information :+++++++++++++++++++++++\n", "[acimpoeru@vis03] out: + There is no planned maintenance taking place this week\n", "[acimpoeru@vis03] out: +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n", "[acimpoeru@vis03] out: \n", "[acimpoeru@vis03] out: [acimpoeru@vis03] rtunnel: opened reverse tunnel: (u'127.0.0.1', 55735) -> ('172.20.1.25', 22) -> ('localhost', 11111)\n", "Connecting to client (reverse connection requested)...\n", "[acimpoeru@vis03] out: Connection URL: csrc://localhost:12000\n", "[acimpoeru@vis03] out: Client connected.\n", "[acimpoeru@vis03] out: " ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get control dictionary" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from zutil.post import get_case_parameters,print_html_parameters\n", "parameters=get_case_parameters(case_name,data_host=data_host,data_dir=data_dir)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get status file" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from zutil.post import get_status_dict\n", "status=get_status_dict(case_name,data_host=data_host,data_dir=data_dir)\n", "num_procs = str(status['num processor'])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Define test conditions - ETW experiment" ] }, { "cell_type": "code", "collapsed": false, "input": [ "alpha = 7.0 # degrees\n", "\n", "Re = 15.0e06 # Reynolds number \n", "\n", "reference_area = 419130.0 # frontal areas in mm^2\n", "\n", "reference_length = 347.09 # mm^2\n", "\n", "reference_span = 1400.0 #mm\n", "\n", "pressure = 295000.0 # Pa\n", "\n", "temperature = 114.0 # i.e. \n", "\n", "gamma = 1.4\n", "\n", "from IPython.display import HTML\n", "HTML(print_html_parameters(parameters))" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
pressure295000.0
temperature114.0
Reynolds No15100000.0
Ref length347.09
Speed0.0
Mach No0.175
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "" ] } ], "prompt_number": 7 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "### Read experimental data from prediction workshop" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import zutil\n", "import zutil.post as post\n", "reload(zutil)\n", "reload(post)\n", "from zutil.post import get_csv_data\n", "import os\n", "import glob\n", "#import pandas as pd\n", "#import hashlib\n", "\n", "#position 1\n", "cp_ps01 = zutil.post.get_csv_data(\"/gpfs/cfms/workarea/projects/hyper_flux/high_lift/results/results_csv_format/cp_ps01.csv\",header=True,remote=True,delim=',')\n", "cp_ps01_flap = zutil.post.get_csv_data(\"/gpfs/cfms/workarea/projects/hyper_flux/high_lift/results/results_csv_format/cp_ps01_flap.csv\",header=True,remote=True,delim=',')\n", "cp_ps01_slat = zutil.post.get_csv_data(\"/gpfs/cfms/workarea/projects/hyper_flux/high_lift/results/results_csv_format/cp_ps01_slat.csv\",header=True,remote=True,delim=',')\n", "\n", "#position 2\n", "cp_ps02 = zutil.post.get_csv_data(\"/gpfs/cfms/workarea/projects/hyper_flux/high_lift/results/results_csv_format/cp_ps02.csv\",header=True,remote=True,delim=',')\n", "cp_ps02_flap = zutil.post.get_csv_data(\"/gpfs/cfms/workarea/projects/hyper_flux/high_lift/results/results_csv_format/cp_ps02_flap.csv\",header=True,remote=True,delim=',')\n", "cp_ps02_slat = zutil.post.get_csv_data(\"/gpfs/cfms/workarea/projects/hyper_flux/high_lift/results/results_csv_format/cp_ps02_slat.csv\",header=True,remote=True,delim=',')\n", "\n", "#position 4\n", "cp_ps04 = zutil.post.get_csv_data(\"/gpfs/cfms/workarea/projects/hyper_flux/high_lift/results/results_csv_format/cp_ps04.csv\",header=True,remote=True,delim=',')\n", "cp_ps04_flap = zutil.post.get_csv_data(\"/gpfs/cfms/workarea/projects/hyper_flux/high_lift/results/results_csv_format/cp_ps04_flap.csv\",header=True,remote=True,delim=',')\n", "cp_ps04_slat = zutil.post.get_csv_data(\"/gpfs/cfms/workarea/projects/hyper_flux/high_lift/results/results_csv_format/cp_ps04_slat.csv\",header=True,remote=True,delim=',')\n", "\n", "\n", "#position 5\n", "\n", "cp_ps05 = zutil.post.get_csv_data(\"/gpfs/cfms/workarea/projects/hyper_flux/high_lift/results/results_csv_format/cp_ps05.csv\",header=True,remote=True,delim=',')\n", "cp_ps05_flap = zutil.post.get_csv_data(\"/gpfs/cfms/workarea/projects/hyper_flux/high_lift/results/results_csv_format/cp_ps05_flap.csv\",header=True,remote=True,delim=',')\n", "cp_ps05_slat = zutil.post.get_csv_data(\"/gpfs/cfms/workarea/projects/hyper_flux/high_lift/results/results_csv_format/cp_ps05_slat.csv\",header=True,remote=True,delim=',')\n", "\n", "#position 6\n", "cp_ps06 = zutil.post.get_csv_data(\"/gpfs/cfms/workarea/projects/hyper_flux/high_lift/results/results_csv_format/cp_ps06.csv\",header=True,remote=True,delim=',')\n", "cp_ps06_flap = zutil.post.get_csv_data(\"/gpfs/cfms/workarea/projects/hyper_flux/high_lift/results/results_csv_format/cp_ps06_flap.csv\",header=True,remote=True,delim=',')\n", "cp_ps06_slat = zutil.post.get_csv_data(\"/gpfs/cfms/workarea/projects/hyper_flux/high_lift/results/results_csv_format/cp_ps06_slat.csv\",header=True,remote=True,delim=',')\n", "\n", "#position 7\n", "\n", "cp_ps07 = zutil.post.get_csv_data(\"/gpfs/cfms/workarea/projects/hyper_flux/high_lift/results/results_csv_format/cp_ps07.csv\",header=True,remote=True,delim=',')\n", "cp_ps07_flap = zutil.post.get_csv_data(\"/gpfs/cfms/workarea/projects/hyper_flux/high_lift/results/results_csv_format/cp_ps07_flap.csv\",header=True,remote=True,delim=',')\n", "cp_ps07_slat = zutil.post.get_csv_data(\"/gpfs/cfms/workarea/projects/hyper_flux/high_lift/results/results_csv_format/cp_ps07_slat.csv\",header=True,remote=True,delim=',')\n", "\n", "#position 8\n", "cp_ps08 = zutil.post.get_csv_data(\"/gpfs/cfms/workarea/projects/hyper_flux/high_lift/results/results_csv_format/cp_ps08.csv\",header=True,remote=True,delim=',')\n", "cp_ps08_flap = zutil.post.get_csv_data(\"/gpfs/cfms/workarea/projects/hyper_flux/high_lift/results/results_csv_format/cp_ps08_flap.csv\",header=True,remote=True,delim=',')\n", "cp_ps08_slat = zutil.post.get_csv_data(\"/gpfs/cfms/workarea/projects/hyper_flux/high_lift/results/results_csv_format/cp_ps08_slat.csv\",header=True,remote=True,delim=',')\n", "\n", "\n", "#position 9\n", "cp_ps09 = zutil.post.get_csv_data(\"/gpfs/cfms/workarea/projects/hyper_flux/high_lift/results/results_csv_format/cp_ps09.csv\",header=True,remote=True,delim=',')\n", "cp_ps09_flap = zutil.post.get_csv_data(\"/gpfs/cfms/workarea/projects/hyper_flux/high_lift/results/results_csv_format/cp_ps09_flap.csv\",header=True,remote=True,delim=',')\n", "cp_ps09_slat = zutil.post.get_csv_data(\"/gpfs/cfms/workarea/projects/hyper_flux/high_lift/results/results_csv_format/cp_ps09_slat.csv\",header=True,remote=True,delim=',')\n", "\n", "#position 10\n", "cp_ps10 = zutil.post.get_csv_data(\"/gpfs/cfms/workarea/projects/hyper_flux/high_lift/results/results_csv_format/cp_ps10.csv\",header=True,remote=True,delim=',')\n", "cp_ps10_flap = zutil.post.get_csv_data(\"/gpfs/cfms/workarea/projects/hyper_flux/high_lift/results/results_csv_format/cp_ps10_flap.csv\",header=True,remote=True,delim=',')\n", "cp_ps10_slat = zutil.post.get_csv_data(\"/gpfs/cfms/workarea/projects/hyper_flux/high_lift/results/results_csv_format/cp_ps10_slat.csv\",header=True,remote=True,delim=',')\n", "\n", "#position 11\n", "\n", "cp_ps11 = zutil.post.get_csv_data(\"/gpfs/cfms/workarea/projects/hyper_flux/high_lift/results/results_csv_format/cp_ps11.csv\",header=True,remote=True,delim=',')\n", "cp_ps11_flap = zutil.post.get_csv_data(\"/gpfs/cfms/workarea/projects/hyper_flux/high_lift/results/results_csv_format/cp_ps11_flap.csv\",header=True,remote=True,delim=',')\n", "cp_ps11_slat = zutil.post.get_csv_data(\"/gpfs/cfms/workarea/projects/hyper_flux/high_lift/results/results_csv_format/cp_ps11_slat.csv\",header=True,remote=True,delim=',')\n", "\n", "\n", "##### Try to use a loop over those files \n", "\n", "\n", "#path = '/Users/andrei/Documents/zPost/ipynb/Hyper_Flux/2nd_High_Lift_Prediction_Workshop/results_v2/results_csv_format/*.csv'\n", "#myfiles = np.zeros(10) \n", "#files=glob.glob(path) \n", "\n", "#for i in range(0,len(files)):\n", " #print files\n", " #data[i] = zutil.post.get_csv_data(files[i],header=True,remote=False,delim=',')\n", "#a=files[0]\n", "\n", "#exp = zutil.post.get_csv_data(a,header=True,remote=False,delim=',')\n", "\n", " \n", "#print files[0]\n", "#print exp\n", "print cp_ps11_slat.keys()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "['x/c_local', 'eta', 'z/c_local', 'X', 'Y', 'Z', 'cp_ETW_RUN238']\n" ] } ], "prompt_number": 8 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "## Cp profiles at different positions " ] }, { "cell_type": "code", "collapsed": false, "input": [ "import zutil\n", "import zutil.post as post\n", "reload(zutil)\n", "reload(post)\n", "\n", "from zutil.post import cp_profile_wall_from_file\n", "\n", "# ----------------------------WING --------------------------------\n", "#-----1\n", "def wing_plot_cp_profile_1(w_ax,file_root,w_span_loc_1):\n", " force_data = cp_profile_wall_from_file(file_root,\n", " [0.0,1.0,0.0],\n", " [0.0, w_span_loc_1,0.0],\n", " func=wing_plot_array_1,\n", " axis=w_ax,\n", " w_span_loc_1=w_span_loc_1,\n", " alpha=alpha)\n", " \n", "w_loop_counter_1 = 0 \n", "w_loop_data_1 = {} \n", "\n", "def wing_plot_array_1(data_array,pts_array,**kwargs):\n", " global w_loop_counter_1,w_loop_data_1\n", " w_ax = kwargs['axis']\n", " w_span_loc_1 = kwargs['w_span_loc_1']\n", " cp_array = data_array.GetPointData()['cp']\n", " chord_array = data_array.GetPointData()['chord']\n", " w_ax.plot(chord_array, cp_array , 'b.',color='b',label='zCFD SST') \n", " w_loop_data_1[w_loop_counter_1] = (chord_array,cp_array)\n", " w_loop_counter_1 += 1\n", "#-----------2\n", " \n", "def wing_plot_cp_profile_2(w_ax,file_root,w_span_loc_2):\n", " force_data = cp_profile_wall_from_file(file_root,\n", " [0.0,1.0,0.0],\n", " [0.0, w_span_loc_2,0.0],\n", " func=wing_plot_array_2,\n", " axis=w_ax,\n", " w_span_loc_2=w_span_loc_2,\n", " alpha=alpha)\n", " \n", "w_loop_counter_2 = 0 \n", "w_loop_data_2 = {} \n", "\n", "def wing_plot_array_2(data_array,pts_array,**kwargs):\n", " global w_loop_counter_2,w_loop_data_2\n", " w_ax = kwargs['axis']\n", " w_span_loc_2 = kwargs['w_span_loc_2']\n", " cp_array = data_array.GetPointData()['cp']\n", " chord_array = data_array.GetPointData()['chord']\n", " w_ax.plot(chord_array, cp_array , 'b.',color='b',label='zCFD SST') \n", " w_loop_data_2[w_loop_counter_2] = (chord_array,cp_array)\n", " w_loop_counter_2 += 1\n", "\n", "#---------------4\n", "def wing_plot_cp_profile_4(w_ax,file_root,w_span_loc_4):\n", " force_data = cp_profile_wall_from_file(file_root,\n", " [0.0,1.0,0.0],\n", " [0.0, w_span_loc_4,0.0],\n", " func=wing_plot_array_4,\n", " axis=w_ax,\n", " w_span_loc_4=w_span_loc_4,\n", " alpha=alpha)\n", " \n", "w_loop_counter_4 = 0 \n", "w_loop_data_4 = {} \n", "\n", "def wing_plot_array_4(data_array,pts_array,**kwargs):\n", " global w_loop_counter_4,w_loop_data_4\n", " w_ax = kwargs['axis']\n", " w_span_loc_4 = kwargs['w_span_loc_4']\n", " cp_array = data_array.GetPointData()['cp']\n", " chord_array = data_array.GetPointData()['chord']\n", " w_ax.plot(chord_array, cp_array , 'b.',color='b',label='zCFD SST') \n", " w_loop_data_4[w_loop_counter_4] = (chord_array,cp_array)\n", " w_loop_counter_4 += 1\n", "#--------------5\n", "def wing_plot_cp_profile_5(w_ax,file_root,w_span_loc_5):\n", " force_data = cp_profile_wall_from_file(file_root,\n", " [0.0,1.0,0.0],\n", " [0.0, w_span_loc_5,0.0],\n", " func=wing_plot_array_5,\n", " axis=w_ax,\n", " w_span_loc_5=w_span_loc_5,\n", " alpha=alpha)\n", " \n", "w_loop_counter_5 = 0 \n", "w_loop_data_5 = {} \n", "\n", "def wing_plot_array_5(data_array,pts_array,**kwargs):\n", " global w_loop_counter_5,w_loop_data_5\n", " w_ax = kwargs['axis']\n", " w_span_loc_5 = kwargs['w_span_loc_5']\n", " cp_array = data_array.GetPointData()['cp']\n", " chord_array = data_array.GetPointData()['chord']\n", " w_ax.plot(chord_array, cp_array , 'b.',color='b',label='zCFD SST') \n", " w_loop_data_5[w_loop_counter_5] = (chord_array,cp_array)\n", " w_loop_counter_5 += 1\n", "#------------6 \n", "def wing_plot_cp_profile_6(w_ax,file_root,w_span_loc_6):\n", " force_data = cp_profile_wall_from_file(file_root,\n", " [0.0,1.0,0.0],\n", " [0.0, w_span_loc_6,0.0],\n", " func=wing_plot_array_6,\n", " axis=w_ax,\n", " w_span_loc_6=w_span_loc_6,\n", " alpha=alpha)\n", " \n", "w_loop_counter_6 = 0 \n", "w_loop_data_6 = {} \n", "\n", "def wing_plot_array_6(data_array,pts_array,**kwargs):\n", " global w_loop_counter_6,w_loop_data_6\n", " w_ax = kwargs['axis']\n", " w_span_loc_6 = kwargs['w_span_loc_6']\n", " cp_array = data_array.GetPointData()['cp']\n", " chord_array = data_array.GetPointData()['chord']\n", " w_ax.plot(chord_array, cp_array , 'b.',color='b',label='zCFD SST') \n", " w_loop_data_6[w_loop_counter_6] = (chord_array,cp_array)\n", " w_loop_counter_6 += 1\n", "#------------7\n", "def wing_plot_cp_profile_7(w_ax,file_root,w_span_loc_7):\n", " force_data = cp_profile_wall_from_file(file_root,\n", " [0.0,1.0,0.0],\n", " [0.0, w_span_loc_7,0.0],\n", " func=wing_plot_array_7,\n", " axis=w_ax,\n", " w_span_loc_7=w_span_loc_7,\n", " alpha=alpha)\n", " \n", "w_loop_counter_7 = 0 \n", "w_loop_data_7 = {} \n", "\n", "def wing_plot_array_7(data_array,pts_array,**kwargs):\n", " global w_loop_counter_7,w_loop_data_7\n", " w_ax = kwargs['axis']\n", " w_span_loc_7 = kwargs['w_span_loc_7']\n", " cp_array = data_array.GetPointData()['cp']\n", " chord_array = data_array.GetPointData()['chord']\n", " w_ax.plot(chord_array, cp_array , 'b.',color='b',label='zCFD SST') \n", " w_loop_data_7[w_loop_counter_7] = (chord_array,cp_array)\n", " w_loop_counter_7 += 1\n", "#------------8\n", "def wing_plot_cp_profile_8(w_ax,file_root,w_span_loc_8):\n", " force_data = cp_profile_wall_from_file(file_root,\n", " [0.0,1.0,0.0],\n", " [0.0, w_span_loc_8,0.0],\n", " func=wing_plot_array_8,\n", " axis=w_ax,\n", " w_span_loc_8=w_span_loc_8,\n", " alpha=alpha)\n", " \n", "w_loop_counter_8 = 0 \n", "w_loop_data_8 = {} \n", "\n", "def wing_plot_array_8(data_array,pts_array,**kwargs):\n", " global w_loop_counter_8,w_loop_data_8\n", " w_ax = kwargs['axis']\n", " w_span_loc_8 = kwargs['w_span_loc_8']\n", " cp_array = data_array.GetPointData()['cp']\n", " chord_array = data_array.GetPointData()['chord']\n", " w_ax.plot(chord_array, cp_array , 'b.',color='b',label='zCFD SST') \n", " w_loop_data_8[w_loop_counter_8] = (chord_array,cp_array)\n", " w_loop_counter_8 += 1\n", "\n", "#------------9\n", "def wing_plot_cp_profile_9(w_ax,file_root,w_span_loc_9):\n", " force_data = cp_profile_wall_from_file(file_root,\n", " [0.0,1.0,0.0],\n", " [0.0, w_span_loc_9,0.0],\n", " func=wing_plot_array_9,\n", " axis=w_ax,\n", " w_span_loc_9=w_span_loc_9,\n", " alpha=alpha)\n", " \n", "w_loop_counter_9 = 0 \n", "w_loop_data_9 = {} \n", "\n", "def wing_plot_array_9(data_array,pts_array,**kwargs):\n", " global w_loop_counter_9,w_loop_data_9\n", " w_ax = kwargs['axis']\n", " w_span_loc_9 = kwargs['w_span_loc_9']\n", " cp_array = data_array.GetPointData()['cp']\n", " chord_array = data_array.GetPointData()['chord']\n", " w_ax.plot(chord_array, cp_array , 'b.',color='b',label='zCFD SST') \n", " w_loop_data_9[w_loop_counter_9] = (chord_array,cp_array)\n", " w_loop_counter_9 += 1\n", "#------------10\n", "def wing_plot_cp_profile_10(w_ax,file_root,w_span_loc_10):\n", " force_data = cp_profile_wall_from_file(file_root,\n", " [0.0,1.0,0.0],\n", " [0.0, w_span_loc_10,0.0],\n", " func=wing_plot_array_10,\n", " axis=w_ax,\n", " w_span_loc_10=w_span_loc_10,\n", " alpha=alpha)\n", " \n", "w_loop_counter_10 = 0 \n", "w_loop_data_10 = {} \n", "\n", "def wing_plot_array_10(data_array,pts_array,**kwargs):\n", " global w_loop_counter_10,w_loop_data_10\n", " w_ax = kwargs['axis']\n", " w_span_loc_10 = kwargs['w_span_loc_10']\n", " cp_array = data_array.GetPointData()['cp']\n", " chord_array = data_array.GetPointData()['chord']\n", " w_ax.plot(chord_array, cp_array , 'b.',color='b',label='zCFD SST') \n", " w_loop_data_10[w_loop_counter_10] = (chord_array,cp_array)\n", " w_loop_counter_10 += 1\n", "#------------11\n", "def wing_plot_cp_profile_11(w_ax,file_root,w_span_loc_11):\n", " force_data = cp_profile_wall_from_file(file_root,\n", " [0.0,1.0,0.0],\n", " [0.0, w_span_loc_11,0.0],\n", " func=wing_plot_array_11,\n", " axis=w_ax,\n", " w_span_loc_11=w_span_loc_11,\n", " alpha=alpha)\n", " \n", "w_loop_counter_11 = 0 \n", "w_loop_data_11 = {} \n", "\n", "def wing_plot_array_11(data_array,pts_array,**kwargs):\n", " global w_loop_counter_11,w_loop_data_11\n", " w_ax = kwargs['axis']\n", " w_span_loc_11 = kwargs['w_span_loc_11']\n", " cp_array = data_array.GetPointData()['cp']\n", " chord_array = data_array.GetPointData()['chord']\n", " w_ax.plot(chord_array, cp_array , 'b.',color='b',label='zCFD SST') \n", " w_loop_data_11[w_loop_counter_11] = (chord_array,cp_array)\n", " w_loop_counter_11 += 1\n", " \n", "# ----------------------------SLAT----------------------------------- \n", "# ------------1\n", "def slat_plot_cp_profile_1(ax,file_root,s_x_loc_1,s_y_loc_1,s_z_loc_1):\n", " force_data = cp_profile_wall_from_file(file_root,\n", " [0.003861,0.999926,0.011494], # the normal of the plane ----constant for the slat\n", " [s_x_loc_1,s_y_loc_1,s_z_loc_1], # definition of the points \n", " func=slat_plot_array_1,\n", " axis=ax,\n", " s_x_loc_1=s_x_loc_1,\n", " s_y_loc_1=s_y_loc_1,\n", " s_z_loc_1=s_z_loc_1,\n", " alpha=alpha)\n", "s_loop_counter_1 = 0 \n", "s_loop_data_1 = {} \n", "\n", "def slat_plot_array_1(data_array,pts_array,**kwargs):\n", " global s_loop_counter_1,s_loop_data_1\n", " ax = kwargs['axis']\n", " #s_span_loc = kwargs['s_span_loc']\n", " s_x_loc_1 = kwargs['s_x_loc_1']\n", " s_y_loc_1 = kwargs['s_y_loc_1']\n", " s_z_loc_1 = kwargs['s_z_loc_1']\n", " cp_array = data_array.GetPointData()['cp']\n", " chord_array = data_array.GetPointData()['chord']\n", " ax.plot(chord_array, cp_array , 'b.',color='b',label='zCFD SST') \n", " #s_loop_data[s_loop_counter] = (pts_array,cp_array)\n", " s_loop_data_1[s_loop_counter_1] = (chord_array,cp_array)\n", " s_loop_counter_1 += 1\n", "#----------------2 \n", "def slat_plot_cp_profile_2(ax,file_root,s_x_loc_2,s_y_loc_2,s_z_loc_2):\n", " force_data = cp_profile_wall_from_file(file_root,\n", " [0.003861,0.999926,0.011494], # the normal of the plane ----constant for the slat\n", " [s_x_loc_2,s_y_loc_2,s_z_loc_2], # definition of the points \n", " func=slat_plot_array_2,\n", " axis=ax,\n", " s_x_loc_2=s_x_loc_2,\n", " s_y_loc_2=s_y_loc_2,\n", " s_z_loc_2=s_z_loc_2,\n", " alpha=alpha)\n", "s_loop_counter_2 = 0 \n", "s_loop_data_2 = {} \n", "\n", "def slat_plot_array_2(data_array,pts_array,**kwargs):\n", " global s_loop_counter_2,s_loop_data_2\n", " ax = kwargs['axis']\n", " #s_span_loc = kwargs['s_span_loc']\n", " s_x_loc_2 = kwargs['s_x_loc_2']\n", " s_y_loc_2 = kwargs['s_y_loc_2']\n", " s_z_loc_2 = kwargs['s_z_loc_2']\n", " cp_array = data_array.GetPointData()['cp']\n", " chord_array = data_array.GetPointData()['chord']\n", " ax.plot(chord_array, cp_array , 'b.',color='b',label='zCFD SST') \n", " #s_loop_data[s_loop_counter] = (pts_array,cp_array)\n", " s_loop_data_2[s_loop_counter_2] = (chord_array,cp_array)\n", " s_loop_counter_2 += 1\n", "#----------------4\n", "def slat_plot_cp_profile_4(ax,file_root,s_x_loc_4,s_y_loc_4,s_z_loc_4):\n", " force_data = cp_profile_wall_from_file(file_root,\n", " [0.003861,0.999926,0.011494], # the normal of the plane ----constant for the slat\n", " [s_x_loc_4,s_y_loc_4,s_z_loc_4], # definition of the points \n", " func=slat_plot_array_4,\n", " axis=ax,\n", " s_x_loc_4=s_x_loc_4,\n", " s_y_loc_4=s_y_loc_4,\n", " s_z_loc_4=s_z_loc_4,\n", " alpha=alpha)\n", "s_loop_counter_4 = 0 \n", "s_loop_data_4 = {} \n", "\n", "def slat_plot_array_4(data_array,pts_array,**kwargs):\n", " global s_loop_counter_4,s_loop_data_4\n", " ax = kwargs['axis']\n", " #s_span_loc = kwargs['s_span_loc']\n", " s_x_loc_4 = kwargs['s_x_loc_4']\n", " s_y_loc_4 = kwargs['s_y_loc_4']\n", " s_z_loc_4 = kwargs['s_z_loc_4']\n", " cp_array = data_array.GetPointData()['cp']\n", " chord_array = data_array.GetPointData()['chord']\n", " ax.plot(chord_array, cp_array , 'b.',color='b',label='zCFD SST') \n", " #s_loop_data[s_loop_counter] = (pts_array,cp_array)\n", " s_loop_data_4[s_loop_counter_4] = (chord_array,cp_array)\n", " s_loop_counter_4 += 1\n", "#----------------5\n", " \n", "def slat_plot_cp_profile_5(ax,file_root,s_x_loc_5,s_y_loc_5,s_z_loc_5):\n", " force_data = cp_profile_wall_from_file(file_root,\n", " [0.003861,0.999926,0.011494], # the normal of the plane ----constant for the slat\n", " [s_x_loc_5,s_y_loc_5,s_z_loc_5], # definition of the points \n", " func=slat_plot_array_5,\n", " axis=ax,\n", " s_x_loc_5=s_x_loc_5,\n", " s_y_loc_5=s_y_loc_5,\n", " s_z_loc_5=s_z_loc_5,\n", " alpha=alpha)\n", "s_loop_counter_5 = 0 \n", "s_loop_data_5 = {} \n", "\n", "def slat_plot_array_5(data_array,pts_array,**kwargs):\n", " global s_loop_counter_5,s_loop_data_5\n", " ax = kwargs['axis']\n", " #s_span_loc = kwargs['s_span_loc']\n", " s_x_loc_5 = kwargs['s_x_loc_5']\n", " s_y_loc_5 = kwargs['s_y_loc_5']\n", " s_z_loc_5 = kwargs['s_z_loc_5']\n", " cp_array = data_array.GetPointData()['cp']\n", " chord_array = data_array.GetPointData()['chord']\n", " ax.plot(chord_array, cp_array , 'b.',color='b',label='zCFD SST') \n", " #s_loop_data[s_loop_counter] = (pts_array,cp_array)\n", " s_loop_data_5[s_loop_counter_5] = (chord_array,cp_array)\n", " s_loop_counter_5 += 1\n", "#---------------6\n", "def slat_plot_cp_profile_6(ax,file_root,s_x_loc_6,s_y_loc_6,s_z_loc_6):\n", " force_data = cp_profile_wall_from_file(file_root,\n", " [0.003861,0.999926,0.011494], # the normal of the plane ----constant for the slat\n", " [s_x_loc_6,s_y_loc_6,s_z_loc_6], # definition of the points \n", " func=slat_plot_array_6,\n", " axis=ax,\n", " s_x_loc_6=s_x_loc_6,\n", " s_y_loc_6=s_y_loc_6,\n", " s_z_loc_6=s_z_loc_6,\n", " alpha=alpha)\n", "s_loop_counter_6 = 0 \n", "s_loop_data_6 = {} \n", "\n", "def slat_plot_array_6(data_array,pts_array,**kwargs):\n", " global s_loop_counter_6,s_loop_data_6\n", " ax = kwargs['axis']\n", " #s_span_loc = kwargs['s_span_loc']\n", " s_x_loc_6 = kwargs['s_x_loc_6']\n", " s_y_loc_6 = kwargs['s_y_loc_6']\n", " s_z_loc_6 = kwargs['s_z_loc_6']\n", " cp_array = data_array.GetPointData()['cp']\n", " chord_array = data_array.GetPointData()['chord']\n", " ax.plot(chord_array, cp_array , 'b.',color='b',label='zCFD SST') \n", " #s_loop_data[s_loop_counter] = (pts_array,cp_array)\n", " s_loop_data_6[s_loop_counter_6] = (chord_array,cp_array)\n", " s_loop_counter_6 += 1\n", "\n", "#--------------7\n", "def slat_plot_cp_profile_7(ax,file_root,s_x_loc_7,s_y_loc_7,s_z_loc_7):\n", " force_data = cp_profile_wall_from_file(file_root,\n", " [0.003861,0.999926,0.011494], # the normal of the plane ----constant for the slat\n", " [s_x_loc_7,s_y_loc_7,s_z_loc_7], # definition of the points \n", " func=slat_plot_array_7,\n", " axis=ax,\n", " s_x_loc_7=s_x_loc_7,\n", " s_y_loc_7=s_y_loc_7,\n", " s_z_loc_7=s_z_loc_7,\n", " alpha=alpha)\n", "s_loop_counter_7 = 0 \n", "s_loop_data_7 = {} \n", "\n", "def slat_plot_array_7(data_array,pts_array,**kwargs):\n", " global s_loop_counter_7,s_loop_data_7\n", " ax = kwargs['axis']\n", " #s_span_loc = kwargs['s_span_loc']\n", " s_x_loc_7 = kwargs['s_x_loc_7']\n", " s_y_loc_7 = kwargs['s_y_loc_7']\n", " s_z_loc_7 = kwargs['s_z_loc_7']\n", " cp_array = data_array.GetPointData()['cp']\n", " chord_array = data_array.GetPointData()['chord']\n", " ax.plot(chord_array, cp_array , 'b.',color='b',label='zCFD SST') \n", " #s_loop_data[s_loop_counter] = (pts_array,cp_array)\n", " s_loop_data_7[s_loop_counter_7] = (chord_array,cp_array)\n", " s_loop_counter_7 += 1\n", "#---------------8\n", "def slat_plot_cp_profile_8(ax,file_root,s_x_loc_8,s_y_loc_8,s_z_loc_8):\n", " force_data = cp_profile_wall_from_file(file_root,\n", " [0.003861,0.999926,0.011494], # the normal of the plane ----constant for the slat\n", " [s_x_loc_8,s_y_loc_8,s_z_loc_8], # definition of the points \n", " func=slat_plot_array_8,\n", " axis=ax,\n", " s_x_loc_8=s_x_loc_8,\n", " s_y_loc_8=s_y_loc_8,\n", " s_z_loc_8=s_z_loc_8,\n", " alpha=alpha)\n", "s_loop_counter_8 = 0 \n", "s_loop_data_8 = {} \n", "\n", "def slat_plot_array_8(data_array,pts_array,**kwargs):\n", " global s_loop_counter_8,s_loop_data_8\n", " ax = kwargs['axis']\n", " #s_span_loc = kwargs['s_span_loc']\n", " s_x_loc_8 = kwargs['s_x_loc_8']\n", " s_y_loc_8 = kwargs['s_y_loc_8']\n", " s_z_loc_8 = kwargs['s_z_loc_8']\n", " cp_array = data_array.GetPointData()['cp']\n", " chord_array = data_array.GetPointData()['chord']\n", " ax.plot(chord_array, cp_array , 'b.',color='b',label='zCFD SST') \n", " #s_loop_data[s_loop_counter] = (pts_array,cp_array)\n", " s_loop_data_8[s_loop_counter_8] = (chord_array,cp_array)\n", " s_loop_counter_8 += 1\n", "#--------------9\n", "def slat_plot_cp_profile_9(ax,file_root,s_x_loc_9,s_y_loc_9,s_z_loc_9):\n", " force_data = cp_profile_wall_from_file(file_root,\n", " [0.003861,0.999926,0.011494], # the normal of the plane ----constant for the slat\n", " [s_x_loc_9,s_y_loc_9,s_z_loc_9], # definition of the points \n", " func=slat_plot_array_9,\n", " axis=ax,\n", " s_x_loc_9=s_x_loc_9,\n", " s_y_loc_9=s_y_loc_9,\n", " s_z_loc_9=s_z_loc_9,\n", " alpha=alpha)\n", "s_loop_counter_9 = 0 \n", "s_loop_data_9 = {} \n", "\n", "def slat_plot_array_9(data_array,pts_array,**kwargs):\n", " global s_loop_counter_9,s_loop_data_9\n", " ax = kwargs['axis']\n", " #s_span_loc = kwargs['s_span_loc']\n", " s_x_loc_9 = kwargs['s_x_loc_9']\n", " s_y_loc_9 = kwargs['s_y_loc_9']\n", " s_z_loc_9 = kwargs['s_z_loc_9']\n", " cp_array = data_array.GetPointData()['cp']\n", " chord_array = data_array.GetPointData()['chord']\n", " ax.plot(chord_array, cp_array , 'b.',color='b',label='zCFD SST') \n", " #s_loop_data[s_loop_counter] = (pts_array,cp_array)\n", " s_loop_data_9[s_loop_counter_9] = (chord_array,cp_array)\n", " s_loop_counter_9 += 1\n", "#------------------10\n", "def slat_plot_cp_profile_10(ax,file_root,s_x_loc,s_y_loc,s_z_loc):\n", " force_data = cp_profile_wall_from_file(file_root,\n", " [0.003861,0.999926,0.011494], # the normal of the plane ----constant for the slat\n", " [s_x_loc_10,s_y_loc_10,s_z_loc_10], # definition of the points \n", " func=slat_plot_array_10,\n", " axis=ax,\n", " s_x_loc_10=s_x_loc_10,\n", " s_y_loc_10=s_y_loc_10,\n", " s_z_loc_10=s_z_loc_10,\n", " alpha=alpha)\n", "s_loop_counter_10 = 0 \n", "s_loop_data_10 = {} \n", "\n", "def slat_plot_array_10(data_array,pts_array,**kwargs):\n", " global s_loop_counter_10,s_loop_data_10\n", " ax = kwargs['axis']\n", " #s_span_loc = kwargs['s_span_loc']\n", " s_x_loc_10 = kwargs['s_x_loc_10']\n", " s_y_loc_10 = kwargs['s_y_loc_10']\n", " s_z_loc_10 = kwargs['s_z_loc_10']\n", " cp_array = data_array.GetPointData()['cp']\n", " chord_array = data_array.GetPointData()['chord']\n", " ax.plot(chord_array, cp_array , 'b.',color='b',label='zCFD SST') \n", " #s_loop_data[s_loop_counter] = (pts_array,cp_array)\n", " s_loop_data_10[s_loop_counter_10] = (chord_array,cp_array)\n", " s_loop_counter_10 += 1\n", "#--------------------11\n", "def slat_plot_cp_profile_11(ax,file_root,s_x_loc,s_y_loc,s_z_loc):\n", " force_data = cp_profile_wall_from_file(file_root,\n", " [0.003861,0.999926,0.011494], # the normal of the plane ----constant for the slat\n", " [s_x_loc_11,s_y_loc_11,s_z_loc_11], # definition of the points \n", " func=slat_plot_array_11,\n", " axis=ax,\n", " s_x_loc_11=s_x_loc_11,\n", " s_y_loc_11=s_y_loc_11,\n", " s_z_loc_11=s_z_loc_11,\n", " alpha=alpha)\n", "s_loop_counter_11 = 0 \n", "s_loop_data_11 = {} \n", "\n", "def slat_plot_array_11(data_array,pts_array,**kwargs):\n", " global s_loop_counter_11,s_loop_data_11\n", " ax = kwargs['axis']\n", " #s_span_loc = kwargs['s_span_loc']\n", " s_x_loc_11 = kwargs['s_x_loc_11']\n", " s_y_loc_11 = kwargs['s_y_loc_11']\n", " s_z_loc_11 = kwargs['s_z_loc_11']\n", " cp_array = data_array.GetPointData()['cp']\n", " chord_array = data_array.GetPointData()['chord']\n", " ax.plot(chord_array, cp_array , 'b.',color='b',label='zCFD SST') \n", " #s_loop_data[s_loop_counter] = (pts_array,cp_array)\n", " s_loop_data_11[s_loop_counter_11] = (chord_array,cp_array)\n", " s_loop_counter_11 += 1\n", " \n", "#----------------------------------------------------------------------\n", "#------------------------------------FLAP------------------------------ \n", "#--------------1\n", "def f_plot_cp_profile_1(f_ax,file_root,f_nx_1,f_ny_1,f_nz_1,f_x_loc_1,f_y_loc_1,f_z_loc_1):\n", " force_data = cp_profile_wall_from_file(file_root,\n", " [f_nx_1,f_ny_1,f_nz_1], # the normal of the plane -----to revise \n", " [f_x_loc_1,f_y_loc_1,f_z_loc_1], # points definition \n", " func=flap_plot_array_1,\n", " axis=f_ax,\n", " f_nx_1 = f_nx_1,\n", " f_ny_1 = f_ny_1,\n", " f_nz_1 = f_nz_1,\n", " f_x_loc_1 = f_x_loc_1,\n", " f_y_loc_1 = f_y_loc_1,\n", " f_z_loc_1 = f_z_loc_1,\n", " alpha=alpha)\n", "f_loop_counter_1 = 0 \n", "f_loop_data_1 = {} \n", "def flap_plot_array_1(data_array,pts_array,**kwargs):\n", " global f_loop_counter_1,f_loop_data_1\n", " f_ax = kwargs['axis']\n", " f_nx_1 = kwargs['f_nx_1']\n", " f_ny_1 = kwargs['f_ny_1']\n", " f_nz_1 = kwargs['f_nz_1']\n", " f_x_loc_1 = kwargs['f_x_loc_1']\n", " f_y_loc_1 = kwargs['f_y_loc_1']\n", " f_z_loc_1 = kwargs['f_z_loc_1']\n", " cp_array = data_array.GetPointData()['cp']\n", " chord_array = data_array.GetPointData()['chord']\n", " f_ax.plot(chord_array, cp_array , 'b.',color='b',label='zCFD SST') \n", " f_loop_data_1[f_loop_counter_1] = (chord_array,cp_array)\n", " f_loop_counter_1 += 1\n", "#---------------2 \n", "def f_plot_cp_profile_2(f_ax,file_root,f_nx_2,f_ny_2,f_nz_2,f_x_loc_2,f_y_loc_2,f_z_loc_2):\n", " force_data = cp_profile_wall_from_file(file_root,\n", " [f_nx_2,f_ny_2,f_nz_2], # the normal of the plane -----to revise \n", " [f_x_loc_2,f_y_loc_2,f_z_loc_2], # points definition \n", " func=flap_plot_array_2,\n", " axis=f_ax,\n", " f_nx_2 = f_nx_2,\n", " f_ny_2 = f_ny_2,\n", " f_nz_2 = f_nz_2,\n", " f_x_loc_2 = f_x_loc_2,\n", " f_y_loc_2 = f_y_loc_2,\n", " f_z_loc_2 = f_z_loc_2,\n", " alpha=alpha)\n", "f_loop_counter_2 = 0 \n", "f_loop_data_2 = {} \n", "def flap_plot_array_2(data_array,pts_array,**kwargs):\n", " global f_loop_counter_2,f_loop_data_2\n", " f_ax = kwargs['axis']\n", " f_nx_2 = kwargs['f_nx_2']\n", " f_ny_2 = kwargs['f_ny_2']\n", " f_nz_2 = kwargs['f_nz_2']\n", " f_x_loc_2 = kwargs['f_x_loc_2']\n", " f_y_loc_2 = kwargs['f_y_loc_2']\n", " f_z_loc_2 = kwargs['f_z_loc_2']\n", " cp_array = data_array.GetPointData()['cp']\n", " chord_array = data_array.GetPointData()['chord']\n", " f_ax.plot(chord_array, cp_array , 'b.',color='b',label='zCFD SST') \n", " f_loop_data_2[f_loop_counter_2] = (chord_array,cp_array)\n", " f_loop_counter_2 += 1\n", "\n", "#---------------4 \n", "def f_plot_cp_profile_4(f_ax,file_root,f_nx_4,f_ny_4,f_nz_4,f_x_loc_4,f_y_loc_4,f_z_loc_4):\n", " force_data = cp_profile_wall_from_file(file_root,\n", " [f_nx_4,f_ny_4,f_nz_4], # the normal of the plane -----to revise \n", " [f_x_loc_4,f_y_loc_4,f_z_loc_4], # points definition \n", " func=flap_plot_array_4,\n", " axis=f_ax,\n", " f_nx_4 = f_nx_4,\n", " f_ny_4 = f_ny_4,\n", " f_nz_4 = f_nz_4,\n", " f_x_loc_4 = f_x_loc_4,\n", " f_y_loc_4 = f_y_loc_4,\n", " f_z_loc_4 = f_z_loc_4,\n", " alpha=alpha)\n", "f_loop_counter_4 = 0 \n", "f_loop_data_4 = {} \n", "def flap_plot_array_4(data_array,pts_array,**kwargs):\n", " global f_loop_counter_4,f_loop_data_4\n", " f_ax = kwargs['axis']\n", " f_nx_4 = kwargs['f_nx_4']\n", " f_ny_4 = kwargs['f_ny_4']\n", " f_nz_4 = kwargs['f_nz_4']\n", " f_x_loc_4 = kwargs['f_x_loc_4']\n", " f_y_loc_4 = kwargs['f_y_loc_4']\n", " f_z_loc_4 = kwargs['f_z_loc_4']\n", " cp_array = data_array.GetPointData()['cp']\n", " chord_array = data_array.GetPointData()['chord']\n", " f_ax.plot(chord_array, cp_array , 'b.',color='b',label='zCFD SST') \n", " f_loop_data_4[f_loop_counter_4] = (chord_array,cp_array)\n", " f_loop_counter_4 += 1\n", "\n", "#---------------5 \n", "def f_plot_cp_profile_5(f_ax,file_root,f_nx_5,f_ny_5,f_nz_5,f_x_loc_5,f_y_loc_5,f_z_loc_5):\n", " force_data = cp_profile_wall_from_file(file_root,\n", " [f_nx_5,f_ny_5,f_nz_5], # the normal of the plane -----to revise \n", " [f_x_loc_5,f_y_loc_5,f_z_loc_5], # points definition \n", " func=flap_plot_array_5,\n", " axis=f_ax,\n", " f_nx_5 = f_nx_1,\n", " f_ny_5 = f_ny_5,\n", " f_nz_5 = f_nz_5,\n", " f_x_loc_5 = f_x_loc_5,\n", " f_y_loc_5 = f_y_loc_5,\n", " f_z_loc_5 = f_z_loc_5,\n", " alpha=alpha)\n", "f_loop_counter_5 = 0 \n", "f_loop_data_5 = {} \n", "def flap_plot_array_5(data_array,pts_array,**kwargs):\n", " global f_loop_counter_5,f_loop_data_5\n", " f_ax = kwargs['axis']\n", " f_nx_5 = kwargs['f_nx_5']\n", " f_ny_5 = kwargs['f_ny_5']\n", " f_nz_5 = kwargs['f_nz_5']\n", " f_x_loc_5 = kwargs['f_x_loc_5']\n", " f_y_loc_5 = kwargs['f_y_loc_5']\n", " f_z_loc_5 = kwargs['f_z_loc_5']\n", " cp_array = data_array.GetPointData()['cp']\n", " chord_array = data_array.GetPointData()['chord']\n", " f_ax.plot(chord_array, cp_array , 'b.',color='b',label='zCFD SST') \n", " f_loop_data_5[f_loop_counter_5] = (chord_array,cp_array)\n", " f_loop_counter_5 += 1\n", "\n", "\n", "#---------------6 \n", "def f_plot_cp_profile_6(f_ax,file_root,f_nx_6,f_ny_6,f_nz_6,f_x_loc_6,f_y_loc_6,f_z_loc_6):\n", " force_data = cp_profile_wall_from_file(file_root,\n", " [f_nx_6,f_ny_6,f_nz_6], # the normal of the plane -----to revise \n", " [f_x_loc_6,f_y_loc_6,f_z_loc_6], # points definition \n", " func=flap_plot_array_6,\n", " axis=f_ax,\n", " f_nx_6 = f_nx_6,\n", " f_ny_6 = f_ny_6,\n", " f_nz_6 = f_nz_6,\n", " f_x_loc_6 = f_x_loc_6,\n", " f_y_loc_6 = f_y_loc_6,\n", " f_z_loc_6 = f_z_loc_6,\n", " alpha=alpha)\n", "f_loop_counter_6 = 0 \n", "f_loop_data_6 = {} \n", "def flap_plot_array_6(data_array,pts_array,**kwargs):\n", " global f_loop_counter_6,f_loop_data_6\n", " f_ax = kwargs['axis']\n", " f_nx_6 = kwargs['f_nx_6']\n", " f_ny_6 = kwargs['f_ny_6']\n", " f_nz_6 = kwargs['f_nz_6']\n", " f_x_loc_6 = kwargs['f_x_loc_6']\n", " f_y_loc_6 = kwargs['f_y_loc_6']\n", " f_z_loc_6 = kwargs['f_z_loc_6']\n", " cp_array = data_array.GetPointData()['cp']\n", " chord_array = data_array.GetPointData()['chord']\n", " f_ax.plot(chord_array, cp_array , 'b.',color='b',label='zCFD SST') \n", " f_loop_data_6[f_loop_counter_6] = (chord_array,cp_array)\n", " f_loop_counter_6 += 1\n", "\n", "\n", "#---------------7 \n", "def f_plot_cp_profile_7(f_ax,file_root,f_nx_7,f_ny_7,f_nz_7,f_x_loc_7,f_y_loc_7,f_z_loc_7):\n", " force_data = cp_profile_wall_from_file(file_root,\n", " [f_nx_7,f_ny_7,f_nz_7], # the normal of the plane -----to revise \n", " [f_x_loc_7,f_y_loc_7,f_z_loc_7], # points definition \n", " func=flap_plot_array_7,\n", " axis=f_ax,\n", " f_nx_7 = f_nx_7,\n", " f_ny_7 = f_ny_7,\n", " f_nz_7 = f_nz_7,\n", " f_x_loc_7 = f_x_loc_7,\n", " f_y_loc_7 = f_y_loc_7,\n", " f_z_loc_7 = f_z_loc_7,\n", " alpha=alpha)\n", "f_loop_counter_7 = 0 \n", "f_loop_data_7 = {} \n", "def flap_plot_array_7(data_array,pts_array,**kwargs):\n", " global f_loop_counter_7,f_loop_data_7\n", " f_ax = kwargs['axis']\n", " f_nx_7 = kwargs['f_nx_7']\n", " f_ny_7 = kwargs['f_ny_7']\n", " f_nz_7 = kwargs['f_nz_7']\n", " f_x_loc_7 = kwargs['f_x_loc_7']\n", " f_y_loc_7 = kwargs['f_y_loc_7']\n", " f_z_loc_7 = kwargs['f_z_loc_7']\n", " cp_array = data_array.GetPointData()['cp']\n", " chord_array = data_array.GetPointData()['chord']\n", " f_ax.plot(chord_array, cp_array , 'b.',color='b',label='zCFD SST') \n", " f_loop_data_7[f_loop_counter_7] = (chord_array,cp_array)\n", " f_loop_counter_7 += 1\n", "\n", "#---------------8 \n", "def f_plot_cp_profile_8(f_ax,file_root,f_nx_8,f_ny_8,f_nz_8,f_x_loc_8,f_y_loc_8,f_z_loc_8):\n", " force_data = cp_profile_wall_from_file(file_root,\n", " [f_nx_8,f_ny_8,f_nz_8], # the normal of the plane -----to revise \n", " [f_x_loc_8,f_y_loc_8,f_z_loc_8], # points definition \n", " func=flap_plot_array_8,\n", " axis=f_ax,\n", " f_nx_8 = f_nx_8,\n", " f_ny_8 = f_ny_8,\n", " f_nz_8 = f_nz_8,\n", " f_x_loc_8 = f_x_loc_8,\n", " f_y_loc_8 = f_y_loc_8,\n", " f_z_loc_8 = f_z_loc_8,\n", " alpha=alpha)\n", "f_loop_counter_8 = 0 \n", "f_loop_data_8 = {} \n", "def flap_plot_array_8(data_array,pts_array,**kwargs):\n", " global f_loop_counter_8,f_loop_data_8\n", " f_ax = kwargs['axis']\n", " f_nx_8 = kwargs['f_nx_8']\n", " f_ny_8 = kwargs['f_ny_8']\n", " f_nz_8 = kwargs['f_nz_8']\n", " f_x_loc_8 = kwargs['f_x_loc_8']\n", " f_y_loc_8 = kwargs['f_y_loc_8']\n", " f_z_loc_8 = kwargs['f_z_loc_8']\n", " cp_array = data_array.GetPointData()['cp']\n", " chord_array = data_array.GetPointData()['chord']\n", " f_ax.plot(chord_array, cp_array , 'b.',color='b',label='zCFD SST') \n", " f_loop_data_8[f_loop_counter_8] = (chord_array,cp_array)\n", " f_loop_counter_8 += 1\n", "\n", "#---------------9\n", "def f_plot_cp_profile_9(f_ax,file_root,f_nx_9,f_ny_9,f_nz_9,f_x_loc_9,f_y_loc_9,f_z_loc_9):\n", " force_data = cp_profile_wall_from_file(file_root,\n", " [f_nx_9,f_ny_9,f_nz_9], # the normal of the plane -----to revise \n", " [f_x_loc_9,f_y_loc_9,f_z_loc_9], # points definition \n", " func=flap_plot_array_9,\n", " axis=f_ax,\n", " f_nx_9 = f_nx_9,\n", " f_ny_9 = f_ny_9,\n", " f_nz_9 = f_nz_9,\n", " f_x_loc_9 = f_x_loc_9,\n", " f_y_loc_9 = f_y_loc_9,\n", " f_z_loc_9 = f_z_loc_9,\n", " alpha=alpha)\n", "f_loop_counter_9 = 0 \n", "f_loop_data_9 = {} \n", "def flap_plot_array_9(data_array,pts_array,**kwargs):\n", " global f_loop_counter_9,f_loop_data_9\n", " f_ax = kwargs['axis']\n", " f_nx_9 = kwargs['f_nx_9']\n", " f_ny_9 = kwargs['f_ny_9']\n", " f_nz_9 = kwargs['f_nz_9']\n", " f_x_loc_9 = kwargs['f_x_loc_9']\n", " f_y_loc_9 = kwargs['f_y_loc_9']\n", " f_z_loc_9 = kwargs['f_z_loc_9']\n", " cp_array = data_array.GetPointData()['cp']\n", " chord_array = data_array.GetPointData()['chord']\n", " f_ax.plot(chord_array, cp_array , 'b.',color='b',label='zCFD SST') \n", " f_loop_data_9[f_loop_counter_9] = (chord_array,cp_array)\n", " f_loop_counter_9 += 1\n", "\n", "#---------------10 \n", "def f_plot_cp_profile_10(f_ax,file_root,f_nx_10,f_ny_10,f_nz_10,f_x_loc_10,f_y_loc_10,f_z_loc_10):\n", " force_data = cp_profile_wall_from_file(file_root,\n", " [f_nx_10,f_ny_10,f_nz_10], # the normal of the plane -----to revise \n", " [f_x_loc_10,f_y_loc_10,f_z_loc_10], # points definition \n", " func=flap_plot_array_10,\n", " axis=f_ax,\n", " f_nx_10 = f_nx_10,\n", " f_ny_10 = f_ny_10,\n", " f_nz_10 = f_nz_10,\n", " f_x_loc_10 = f_x_loc_10,\n", " f_y_loc_10 = f_y_loc_10,\n", " f_z_loc_10 = f_z_loc_10,\n", " alpha=alpha)\n", "f_loop_counter_10 = 0 \n", "f_loop_data_10 = {} \n", "def flap_plot_array_10(data_array,pts_array,**kwargs):\n", " global f_loop_counter_10,f_loop_data_10\n", " f_ax = kwargs['axis']\n", " f_nx_10 = kwargs['f_nx_10']\n", " f_ny_10 = kwargs['f_ny_10']\n", " f_nz_10 = kwargs['f_nz_10']\n", " f_x_loc_10 = kwargs['f_x_loc_10']\n", " f_y_loc_10 = kwargs['f_y_loc_10']\n", " f_z_loc_10 = kwargs['f_z_loc_10']\n", " cp_array = data_array.GetPointData()['cp']\n", " chord_array = data_array.GetPointData()['chord']\n", " f_ax.plot(chord_array, cp_array , 'b.',color='b',label='zCFD SST') \n", " f_loop_data_10[f_loop_counter_10] = (chord_array,cp_array)\n", " f_loop_counter_10 += 1\n", "\n", "\n", "#---------------11 \n", "\n", "def f_plot_cp_profile_11(f_ax,file_root,f_nx_11,f_ny_11,f_nz_11,f_x_loc_11,f_y_loc_11,f_z_loc_11):\n", " force_data = cp_profile_wall_from_file(file_root,\n", " [f_nx_11,f_ny_11,f_nz_11], # the normal of the plane -----to revise \n", " [f_x_loc_11,f_y_loc_11,f_z_loc_11], # points definition \n", " func=flap_plot_array_11,\n", " axis=f_ax,\n", " f_nx_11 = f_nx_11,\n", " f_ny_11 = f_ny_11,\n", " f_nz_11 = f_nz_11,\n", " f_x_loc_11 = f_x_loc_11,\n", " f_y_loc_11 = f_y_loc_11,\n", " f_z_loc_11 = f_z_loc_11,\n", " alpha=alpha)\n", "f_loop_counter_11 = 0 \n", "f_loop_data_11 = {} \n", "def flap_plot_array_11(data_array,pts_array,**kwargs):\n", " global f_loop_counter_11,f_loop_data_11\n", " f_ax = kwargs['axis']\n", " f_nx_11 = kwargs['f_nx_11']\n", " f_ny_11 = kwargs['f_ny_11']\n", " f_nz_11 = kwargs['f_nz_11']\n", " f_x_loc_11 = kwargs['f_x_loc_11']\n", " f_y_loc_11 = kwargs['f_y_loc_11']\n", " f_z_loc_11 = kwargs['f_z_loc_11']\n", " cp_array = data_array.GetPointData()['cp']\n", " chord_array = data_array.GetPointData()['chord']\n", " f_ax.plot(chord_array, cp_array , 'b.',color='b',label='zCFD SST') \n", " f_loop_data_11[f_loop_counter_11] = (chord_array,cp_array)\n", " f_loop_counter_11 += 1\n", "\n", "\n", "\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## SLAT DATA SELECTION" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from zutil.post import get_case_root\n", " \n", "\n", " # SLAT POSITIONS \n", " \n", "s_x_loc_1 = 1128.5257830070 ; s_y_loc_1 = 213.764 ; s_z_loc_1 = -54.987 ; \n", "s_x_loc_2 = 1252.6898829811 ; s_y_loc_2 = 407.515 ; s_z_loc_2 = -49.951 ; \n", "s_x_loc_4 = 1393.4388029500 ; s_y_loc_4 = 632.53 ; s_z_loc_4 = -35.956 ;\n", "s_x_loc_5 = 1475.6542901294 ; s_y_loc_5 = 763.513 ; s_z_loc_5 = -27.997 ; \n", "s_x_loc_6 = 1596.4087811939 ; s_y_loc_6 = 955.929 ; s_z_loc_6 = -16.246 ; \n", "s_x_loc_7 = 1626.6927045299 ; s_y_loc_7 = 1004.247 ; s_z_loc_7 = -13.28 ;\n", "s_x_loc_8 = 1658.0724766097 ; s_y_loc_8 = 1054.277 ; s_z_loc_8 = -10.20 ;\n", "s_x_loc_9 = 1717.2411764284 ; s_y_loc_9 = 1148.618 ; s_z_loc_9 = -4.364 ; \n", "s_x_loc_10 = 1781.1352303004 ; s_y_loc_10 = 1250.389 ; s_z_loc_10 = 1.984 ; \n", "s_x_loc_11 = 1845.2888081300 ; s_y_loc_11 = 1352.433 ; s_z_loc_11 = 8.402 ; \n", "\n", "fig1 = pl.figure(figsize=(20, 10),dpi=600, facecolor='w', edgecolor='k')\n", "fig2 = pl.figure(figsize=(20, 10),dpi=600, facecolor='w', edgecolor='k')\n", "fig4 = pl.figure(figsize=(20, 10),dpi=600, facecolor='w', edgecolor='k')\n", "fig5 = pl.figure(figsize=(20, 10),dpi=600, facecolor='w', edgecolor='k')\n", "fig6 = pl.figure(figsize=(20, 10),dpi=600, facecolor='w', edgecolor='k')\n", "fig7 = pl.figure(figsize=(20, 10),dpi=600, facecolor='w', edgecolor='k')\n", "fig8 = pl.figure(figsize=(20, 10),dpi=600, facecolor='w', edgecolor='k')\n", "fig9 = pl.figure(figsize=(20, 10),dpi=600, facecolor='w', edgecolor='k')\n", "fig10 = pl.figure(figsize=(20, 10),dpi=600, facecolor='w', edgecolor='k')\n", "fig11 = pl.figure(figsize=(20, 10),dpi=600, facecolor='w', edgecolor='k')\n", "\n", "\n", "fig1.suptitle('PS1', fontsize=30, fontweight='bold')\n", "fig2.suptitle('PS2', fontsize=30, fontweight='bold')\n", "fig4.suptitle('PS4', fontsize=30, fontweight='bold')\n", "fig5.suptitle('PS5', fontsize=30, fontweight='bold')\n", "fig6.suptitle('PS6', fontsize=30, fontweight='bold')\n", "fig7.suptitle('PS7', fontsize=30, fontweight='bold')\n", "fig8.suptitle('PS8', fontsize=30, fontweight='bold')\n", "fig9.suptitle('PS9', fontsize=30, fontweight='bold')\n", "fig10.suptitle('PS10', fontsize=30, fontweight='bold')\n", "fig11.suptitle('PS11', fontsize=30, fontweight='bold')\n", "\n", "ax = fig1.add_subplot(2,1,1)\n", "ax2 = fig2.add_subplot(2,1,1)\n", "ax4 = fig4.add_subplot(2,1,1)\n", "ax5 = fig5.add_subplot(2,1,1)\n", "ax6 = fig6.add_subplot(2,1,1)\n", "ax7 = fig7.add_subplot(2,1,1)\n", "ax8 = fig8.add_subplot(2,1,1)\n", "ax9 = fig9.add_subplot(2,1,1)\n", "ax10 = fig10.add_subplot(2,1,1)\n", "ax11 = fig11.add_subplot(2,1,1)\n", "\n", "ax.grid(True)\n", "ax.set_xlabel('x/c')\n", "ax.set_ylabel('Cp []')\n", "ax.axis([0,1.1,-5,5])\n", "ax2.axis([0,1.1,-5,5])\n", "ax4.axis([0,1.1,-5,5])\n", "ax5.axis([0,1.1,-5,5])\n", "ax6.axis([0,1.1,-5,5])\n", "ax7.axis([0,1.1,-5,5])\n", "ax8.axis([0,1.1,-5,5])\n", "ax9.axis([0,1.1,-5,5])\n", "ax10.axis([0,1.1,-5,5])\n", "ax11.axis([0,1.1,-5,5])\n", "\n", "# SELECT THE LOOPS OF INTEREST AND NORMALIZE THEM \n", "# PS 1\n", "slat_plot_cp_profile_1(ax,get_case_root(case_name,num_procs),s_x_loc_1,s_y_loc_1,s_z_loc_1)\n", "ax.plot(s_loop_data_1[2][0],s_loop_data_1[2][1], color='r', markersize=20)\n", "\n", "slat_plot_cp_profile_2(ax2,get_case_root(case_name,num_procs),s_x_loc_2,s_y_loc_2,s_z_loc_2)\n", "ax2.plot(s_loop_data_2[2][0],s_loop_data_2[2][1], color='r', markersize=20)\n", "\n", "slat_plot_cp_profile_4(ax4,get_case_root(case_name,num_procs),s_x_loc_4,s_y_loc_4,s_z_loc_4)\n", "ax4.plot(s_loop_data_4[2][0],s_loop_data_4[2][1], color='r', markersize=20)\n", "\n", "# in this case the third block in the loop data dictionary is the slat \n", "slat_plot_cp_profile_5(ax5,get_case_root(case_name,num_procs),s_x_loc_5,s_y_loc_5,s_z_loc_5)\n", "ax5.plot(s_loop_data_5[2][0],s_loop_data_5[2][1], color='r', markersize=20)\n", "# in this case the third block in the loop data dictionary is the slat \n", "slat_plot_cp_profile_6(ax6,get_case_root(case_name,num_procs),s_x_loc_6,s_y_loc_6,s_z_loc_6)\n", "ax6.plot(s_loop_data_6[2][0],s_loop_data_6[2][1], color='r', markersize=20)\n", "\n", "slat_plot_cp_profile_7(ax7,get_case_root(case_name,num_procs),s_x_loc_7,s_y_loc_7,s_z_loc_7)\n", "ax7.plot(s_loop_data_7[2][0],s_loop_data_7[2][1], color='r', markersize=20)\n", "\n", "slat_plot_cp_profile_8(ax8,get_case_root(case_name,num_procs),s_x_loc_8,s_y_loc_8,s_z_loc_8)\n", "ax8.plot(s_loop_data_8[2][0],s_loop_data_8[2][1], color='r', markersize=20)\n", "\n", "slat_plot_cp_profile_9(ax9,get_case_root(case_name,num_procs),s_x_loc_9,s_y_loc_9,s_z_loc_9)\n", "ax9.plot(s_loop_data_9[2][0],s_loop_data_9[2][1], color='r', markersize=20)\n", "\n", "slat_plot_cp_profile_10(ax10,get_case_root(case_name,num_procs),s_x_loc_10,s_y_loc_10,s_z_loc_10)\n", "ax10.plot(s_loop_data_10[2][0],s_loop_data_10[2][1], color='r', markersize=20)\n", "\n", "slat_plot_cp_profile_11(ax11,get_case_root(case_name,num_procs),s_x_loc_10,s_y_loc_10,s_z_loc_10)\n", "ax11.plot(s_loop_data_11[2][0],s_loop_data_11[2][1], color='r', markersize=20)\n", "\n", "# SELECT THE LOOPS OF INTEREST AND NORMALIZE THEM \n", "#ps1\n", "s_min_loop_1 = min(s_loop_data_1[2][0]); s_max_loop_1 = max(s_loop_data_1[2][0]);\n", "s_norm_loop_1 = np.zeros(len(s_loop_data_1[2][0])) ;\n", "s_d1 = s_max_loop_1-s_min_loop_1 ;\n", "for i in range (0,len(s_loop_data_1[2][0])):\n", " s_norm_loop_1[i] = (s_loop_data_1[2][0][i]-s_min_loop_1)/s_d1\n", "#ps2\n", "s_min_loop_2 = min(s_loop_data_2[2][0]); s_max_loop_2 = max(s_loop_data_2[2][0]);\n", "s_norm_loop_2 = np.zeros(len(s_loop_data_2[2][0])) ;\n", "s_d2 = s_max_loop_2-s_min_loop_2 ;\n", "for j in range (0,len(s_loop_data_2[2][0])):\n", " s_norm_loop_2[j] = (s_loop_data_2[2][0][j]-s_min_loop_2)/s_d2\n", "#ps4\n", "s_min_loop_4 = min(s_loop_data_4[2][0]); s_max_loop_4 = max(s_loop_data_4[2][0]);\n", "s_norm_loop_4 = np.zeros(len(s_loop_data_4[2][0])) ;\n", "s_d4 = s_max_loop_4-s_min_loop_4 ;\n", "for i in range (0,len(s_loop_data_4[2][0])):\n", " s_norm_loop_4[i] = (s_loop_data_4[2][0][i]-s_min_loop_4)/s_d4\n", "#ps5\n", "s_min_loop_5 = min(s_loop_data_5[2][0]); s_max_loop_5 = max(s_loop_data_5[2][0]);\n", "s_norm_loop_5 = np.zeros(len(s_loop_data_5[2][0])) ;\n", "s_d5 = s_max_loop_5-s_min_loop_5 ;\n", "for i in range (0,len(s_loop_data_5[2][0])):\n", " s_norm_loop_5[i] = (s_loop_data_5[2][0][i]-s_min_loop_5)/s_d5\n", "#ps6\n", "s_min_loop_6 = min(s_loop_data_6[2][0]); s_max_loop_6 = max(s_loop_data_6[2][0]);\n", "s_norm_loop_6 = np.zeros(len(s_loop_data_6[2][0])) ;\n", "s_d6 = s_max_loop_6-s_min_loop_6 ;\n", "for i in range (0,len(s_loop_data_6[2][0])):\n", " s_norm_loop_6[i] = (s_loop_data_6[2][0][i]-s_min_loop_6)/s_d6\n", "#ps7\n", "s_min_loop_7 = min(s_loop_data_7[2][0]); s_max_loop_7 = max(s_loop_data_7[2][0]);\n", "s_norm_loop_7 = np.zeros(len(s_loop_data_7[2][0])) ;\n", "s_d7 = s_max_loop_7-s_min_loop_7 ;\n", "for i in range (0,len(s_loop_data_7[2][0])):\n", " s_norm_loop_7[i] = (s_loop_data_7[2][0][i]-s_min_loop_7)/s_d7\n", "#ps8\n", "s_min_loop_8 = min(s_loop_data_8[2][0]); s_max_loop_8 = max(s_loop_data_8[2][0]);\n", "s_norm_loop_8 = np.zeros(len(s_loop_data_8[2][0])) ;\n", "s_d8 = s_max_loop_8-s_min_loop_8 ;\n", "for i in range (0,len(s_loop_data_8[2][0])):\n", " s_norm_loop_8[i] = (s_loop_data_8[2][0][i]-s_min_loop_8)/s_d8\n", "\n", "#ps9\n", "s_min_loop_9 = min(s_loop_data_9[2][0]); s_max_loop_9 = max(s_loop_data_9[2][0]);\n", "s_norm_loop_9 = np.zeros(len(s_loop_data_9[2][0])) ;\n", "s_d9 = s_max_loop_9-s_min_loop_9 ;\n", "for i in range (0,len(s_loop_data_9[2][0])):\n", " s_norm_loop_9[i] = (s_loop_data_9[2][0][i]-s_min_loop_9)/s_d9\n", "#ps10\n", "s_min_loop_10 = min(s_loop_data_10[2][0]); s_max_loop_10 = max(s_loop_data_10[2][0]);\n", "s_norm_loop_10 = np.zeros(len(s_loop_data_10[2][0])) ;\n", "s_d10 = s_max_loop_10-s_min_loop_10 ;\n", "for i in range (0,len(s_loop_data_10[2][0])):\n", " s_norm_loop_10[i] = (s_loop_data_10[2][0][i]-s_min_loop_10)/s_d10\n", "#ps11\n", "s_min_loop_11 = min(s_loop_data_11[2][0]); s_max_loop_11 = max(s_loop_data_11[2][0]);\n", "s_norm_loop_11 = np.zeros(len(s_loop_data_11[2][0])) ;\n", "s_d11 = s_max_loop_11-s_min_loop_11 ;\n", "for i in range (0,len(s_loop_data_11[2][0])):\n", " s_norm_loop_11[i] = (s_loop_data_11[2][0][i]-s_min_loop_11)/s_d11\n", "\n", "# the data is stored in loop_data\n", "# For each loop calculate min/max x. Locate the vector in the matrix i.e [0][0] [0][1] .........\n", "\n", "#min_loop_1 = min(s_loop_data[0][0])\n", "#max_loop_1 = max(s_loop_data[0][0])\n", "#print min_loop_1\n", "#print max_loop_1\n", "\n", "#norm_loop_1 = np.zeros(len(loop_data[0][0]))\n", "# Normalise x coordinate\n", "#distancee_l = max_loop_1 - min_loop_1\n", "#print distancee_l\n", "#for i in range(0,len(loop_data[0][0])-1):\n", "# norm_loop_1[i] = (loop_data[0][0][i] - min_loop_1)/(max_loop_1 - min_loop_1)\n", "\n", "#print norm_loop_1 \n", "#print len(norm_loop_1)\n", "#print loop_data[0][0]\n", "\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "//Applications/paraview.app/Contents/Python/paraview/vtk/numpy_interface/dataset_adapter.py:126: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n", " if array == None:\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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0zc3BU9CoUZm+wiWQvLWqQuuDhfuyGW2UrrGSytqVKKqoqHAGDBgQ9euss87q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\nZ2V1yTS6cELnswIAAABAON4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4sVRRIS1ZQhkcmaVvX2nXLuvxtGmK7loZCRcub8mKZcWrV6/W0KFDdfLJJysn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\nvWBP5PbSZd1OXh+P/xfaJ5b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/UdYF5+tvOwZr3s4LtUyXacj4M1V/0NfkAiE1tsSJ9e9u34E4VP2pHll9se7V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\nSwNfpwNtG+lxwt1XtNvG8nW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d26It6q7iYivkSAaDBklHj0qVlVJ+vtSpk9SuY2t9qGJ9oLNVpDVKU50kqWdP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\n743OCo8eNeaNN8y7/W42m9v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"text": [ "" ] } ], "prompt_number": 10 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "## WING DATA SELECTION " ] }, { "cell_type": "code", "collapsed": false, "input": [ "from zutil.post import get_case_root\n", "\n", "w_span_loc_1 = 209.67 ; w_span_loc_2 = 403.87 ; w_span_loc_4 = 629.14 ; w_span_loc_5 = 760.30 ;w_span_loc_6 = 952.95 ; \n", "w_span_loc_7 = 1001.28; w_span_loc_8 = 1051.35; w_span_loc_9 = 1145.79; w_span_loc_10 = 1247.68; w_span_loc_11 = 1348.83 ;\n", "\n", "\n", "\n", "w_fig1 = pl.figure(figsize=(20, 15),dpi=600, facecolor='w', edgecolor='k')\n", "w_fig2 = pl.figure(figsize=(20, 15),dpi=600, facecolor='w', edgecolor='k')\n", "w_fig4 = pl.figure(figsize=(20, 15),dpi=600, facecolor='w', edgecolor='k')\n", "w_fig5 = pl.figure(figsize=(20, 15),dpi=600, facecolor='w', edgecolor='k')\n", "w_fig6 = pl.figure(figsize=(20, 15),dpi=600, facecolor='w', edgecolor='k')\n", "w_fig7 = pl.figure(figsize=(20, 15),dpi=600, facecolor='w', edgecolor='k')\n", "w_fig8 = pl.figure(figsize=(20, 15),dpi=600, facecolor='w', edgecolor='k')\n", "w_fig9 = pl.figure(figsize=(20, 15),dpi=600, facecolor='w', edgecolor='k')\n", "w_fig10 = pl.figure(figsize=(20, 15),dpi=600, facecolor='w', edgecolor='k')\n", "w_fig11 = pl.figure(figsize=(20, 15),dpi=600, facecolor='w', edgecolor='k')\n", "\n", "\n", "w_fig1.suptitle('PS1', fontsize=30, fontweight='bold')\n", "w_fig2.suptitle('PS2', fontsize=30, fontweight='bold')\n", "w_fig4.suptitle('PS4', fontsize=30, fontweight='bold')\n", "w_fig5.suptitle('PS5', fontsize=30, fontweight='bold')\n", "w_fig6.suptitle('PS6', fontsize=30, fontweight='bold')\n", "w_fig7.suptitle('PS7', fontsize=30, fontweight='bold')\n", "w_fig8.suptitle('PS8', fontsize=30, fontweight='bold')\n", "w_fig9.suptitle('PS9', fontsize=30, fontweight='bold')\n", "w_fig10.suptitle('PS10', fontsize=30, fontweight='bold')\n", "w_fig11.suptitle('PS11', fontsize=30, fontweight='bold')\n", "\n", "w_ax =w_fig1.add_subplot(2,1,1)\n", "w_ax2 = w_fig2.add_subplot(2,1,1)\n", "w_ax4 = w_fig4.add_subplot(2,1,1)\n", "w_ax5 = w_fig5.add_subplot(2,1,1)\n", "w_ax6 = w_fig6.add_subplot(2,1,1)\n", "w_ax7 = w_fig7.add_subplot(2,1,1)\n", "w_ax8 = w_fig8.add_subplot(2,1,1)\n", "w_ax9 = w_fig9.add_subplot(2,1,1)\n", "w_ax10 = w_fig10.add_subplot(2,1,1)\n", "w_ax11 = w_fig11.add_subplot(2,1,1)\n", "\n", "w_ax.grid(True)\n", "w_ax.set_xlabel('x/c')\n", "w_ax.set_ylabel('Cp []')\n", "w_ax.axis([0,1.1,-5,5])\n", "w_ax2.axis([0,1.1,-5,5])\n", "w_ax4.axis([0,1.1,-5,5])\n", "w_ax5.axis([0,1.1,-5,5])\n", "w_ax6.axis([0,1.1,-5,5])\n", "w_ax7.axis([0,1.1,-5,5])\n", "w_ax8.axis([0,1.1,-5,5])\n", "w_ax9.axis([0,1.1,-5,5])\n", "w_ax10.axis([0,1.1,-5,5])\n", "w_ax11.axis([0,1.1,-5,5])\n", "\n", "# the wing is in the 0th loop for the 6 9 10 \n", "\n", "wing_plot_cp_profile_1(w_ax,get_case_root(case_name,num_procs),w_span_loc_1)\n", "w_ax.plot(w_loop_data_1[1][0],w_loop_data_1[1][1], color='r', markersize=20)\n", "\n", "wing_plot_cp_profile_2(w_ax2,get_case_root(case_name,num_procs),w_span_loc_2)\n", "w_ax2.plot(w_loop_data_2[1][0],w_loop_data_2[1][1], color='r', markersize=20)\n", "\n", "wing_plot_cp_profile_4(w_ax4,get_case_root(case_name,num_procs),w_span_loc_4)\n", "w_ax4.plot(w_loop_data_4[1][0],w_loop_data_4[1][1], color='r', markersize=20)\n", "\n", "wing_plot_cp_profile_5(w_ax5,get_case_root(case_name,num_procs),w_span_loc_5)\n", "w_ax5.plot(w_loop_data_5[1][0],w_loop_data_5[1][1], color='r', markersize=20)\n", "\n", "wing_plot_cp_profile_6(w_ax6,get_case_root(case_name,num_procs),w_span_loc_6)\n", "w_ax6.plot(w_loop_data_6[1][0],w_loop_data_6[1][1], color='r', markersize=20)\n", "\n", "wing_plot_cp_profile_7(w_ax7,get_case_root(case_name,num_procs),w_span_loc_7)\n", "w_ax7.plot(w_loop_data_7[1][0],w_loop_data_7[1][1], color='r', markersize=20)\n", "\n", "wing_plot_cp_profile_8(w_ax8,get_case_root(case_name,num_procs),w_span_loc_8)\n", "w_ax8.plot(w_loop_data_8[1][0],w_loop_data_8[1][1], color='r', markersize=20)\n", "\n", "wing_plot_cp_profile_9(w_ax9,get_case_root(case_name,num_procs),w_span_loc_9)\n", "w_ax9.plot(w_loop_data_9[1][0],w_loop_data_9[1][1], color='r', markersize=20)\n", "\n", "wing_plot_cp_profile_10(w_ax10,get_case_root(case_name,num_procs),w_span_loc_10)\n", "w_ax10.plot(w_loop_data_10[1][0],w_loop_data_10[1][1], color='r', markersize=20)\n", "\n", "wing_plot_cp_profile_11(w_ax11,get_case_root(case_name,num_procs),w_span_loc_11)\n", "w_ax11.plot(w_loop_data_11[1][0],w_loop_data_11[1][1], color='r', markersize=20)\n", "\n", "\n", "# SELECT THE LOOPS OF INTEREST AND NORMALIZE THEM \n", "# the wing is in the 0th loop for the 6 9 10 \n", "\n", "#ps1\n", "w_min_loop_1 = min(w_loop_data_1[1][0]); w_max_loop_1 = max(w_loop_data_1[1][0]);\n", "w_norm_loop_1 = np.zeros(len(w_loop_data_1[1][0])) ;\n", "w_d1 = w_max_loop_1-w_min_loop_1 ;\n", "for i in range (0,len(w_loop_data_1[1][0])):\n", " w_norm_loop_1[i] = (w_loop_data_1[1][0][i]-w_min_loop_1)/w_d1\n", "#ps2\n", "w_min_loop_2 = min(w_loop_data_2[1][0]); w_max_loop_2 = max(w_loop_data_1[1][0]);\n", "w_norm_loop_2 = np.zeros(len(w_loop_data_2[1][0])) ;\n", "w_d2 = w_max_loop_2-w_min_loop_2 ;\n", "for i in range (0,len(w_loop_data_2[1][0])):\n", " w_norm_loop_2[i] = (w_loop_data_2[1][0][i]-w_min_loop_2)/w_d2\n", "#ps4\n", "w_min_loop_4 = min(w_loop_data_4[1][0]); w_max_loop_4 = max(w_loop_data_4[1][0]);\n", "w_norm_loop_4 = np.zeros(len(w_loop_data_4[1][0])) ;\n", "w_d4 = w_max_loop_4-w_min_loop_4 ;\n", "for i in range (0,len(w_loop_data_4[1][0])):\n", " w_norm_loop_4[i] = (w_loop_data_4[1][0][i]-w_min_loop_4)/w_d4\n", "#ps5\n", "w_min_loop_5 = min(w_loop_data_5[1][0]); w_max_loop_5 = max(w_loop_data_5[1][0]);\n", "w_norm_loop_5 = np.zeros(len(w_loop_data_5[1][0])) ;\n", "w_d5 = w_max_loop_5-w_min_loop_5 ;\n", "for i in range (0,len(w_loop_data_5[1][0])):\n", " w_norm_loop_5[i] = (w_loop_data_5[1][0][i]-w_min_loop_5)/w_d5\n", "#ps6\n", "w_min_loop_6 = min(w_loop_data_6[1][0]); w_max_loop_6 = max(w_loop_data_6[1][0]);\n", "w_norm_loop_6 = np.zeros(len(w_loop_data_6[1][0])) ;\n", "w_d6 = w_max_loop_6-w_min_loop_6 ;\n", "for i in range (0,len(w_loop_data_6[1][0])):\n", " w_norm_loop_6[i] = (w_loop_data_6[1][0][i]-w_min_loop_6)/w_d6\n", "#ps7\n", "w_min_loop_7 = min(w_loop_data_7[1][0]); w_max_loop_7 = max(w_loop_data_7[1][0]);\n", "w_norm_loop_7 = np.zeros(len(w_loop_data_7[1][0])) ;\n", "w_d7 = w_max_loop_7-w_min_loop_7 ;\n", "for i in range (0,len(w_loop_data_7[1][0])):\n", " w_norm_loop_7[i] = (w_loop_data_7[1][0][i]-w_min_loop_7)/w_d7\n", "#ps8\n", "w_min_loop_8 = min(w_loop_data_8[1][0]); w_max_loop_8 = max(w_loop_data_8[1][0]);\n", "w_norm_loop_8 = np.zeros(len(w_loop_data_8[1][0])) ;\n", "w_d8 = w_max_loop_8-w_min_loop_8 ;\n", "for i in range (0,len(w_loop_data_8[1][0])):\n", " w_norm_loop_8[i] = (w_loop_data_8[1][0][i]-w_min_loop_8)/w_d8\n", "#ps9\n", "w_min_loop_9 = min(w_loop_data_9[1][0]); w_max_loop_9 = max(w_loop_data_9[1][0]);\n", "w_norm_loop_9 = np.zeros(len(w_loop_data_9[1][0])) ;\n", "w_d9 = w_max_loop_9-w_min_loop_9 ;\n", "for i in range (0,len(w_loop_data_9[1][0])):\n", " w_norm_loop_9[i] = (w_loop_data_9[1][0][i]-w_min_loop_9)/w_d9\n", "#ps10\n", "w_min_loop_10 = min(w_loop_data_10[1][0]); w_max_loop_10 = max(w_loop_data_10[1][0]);\n", "w_norm_loop_10 = np.zeros(len(w_loop_data_10[1][0])) ;\n", "w_d10 = w_max_loop_10-w_min_loop_10 ;\n", "for i in range (0,len(w_loop_data_10[1][0])):\n", " w_norm_loop_10[i] = (w_loop_data_10[1][0][i]-w_min_loop_10)/w_d10\n", "#ps11\n", "w_min_loop_11 = min(w_loop_data_11[1][0]); w_max_loop_11 = max(w_loop_data_11[1][0]);\n", "w_norm_loop_11 = np.zeros(len(w_loop_data_11[1][0])) ;\n", "w_d11 = w_max_loop_11-w_min_loop_11 ;\n", "for i in range (0,len(w_loop_data_11[1][0])):\n", " w_norm_loop_11[i] = (w_loop_data_11[1][0][i]-w_min_loop_11)/w_d11\n", "\n", " \n", " \n", "\n", "\n", "\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", 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HrFv5+92Ktrt043VuRYe7jRVC3G59PMul/Fy3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\nU094UldnTdaIrMVKTw9jNom/eb3SqlWafeMcDVg/R48W/6L53vM1V+f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H+hQUSJs38/9tKEp52HP++VafqtLHPrTFzx+MhT61tW1VxDtyu83n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\nvx2oXQcCVK5AjTg7ULPuDNTjzwTom+8DVa5Ajb0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hmTfPDGOXaHI1EP3aJA+Hu2jRIHbcXFMjjR8v1dWZXxQAAADgEN8I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\n1WRkSMuWWSN6hg+X+vcP7hIagF00Ffb4vABLRIT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fsUP64ANzLrxnj3TGGblrI3qGYVwyQc/BmjhxovTyy8kAp7JSeuYZ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\nUvqpMOUoTIW+ocooD1WOwjT82lA9/Vao2SvH3b3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xjr3E4MwAAADoLzwe/XroXE1bebcWLLD3TKgC7EuSjQFGj+7/DReQ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\nVUd158RqXdshQoEVbSvssb/2evhhrr0AK3Jp2GO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ME676+q57yAYAAAD6ydrQ8fpJ7bf0hM6hCQuAnNVvzbjefPNNHXzw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\ndW5bjatGURFPLDDdfk2R5q2OVZiKHV7xB3C7U6e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bDQ6Izz+XJkxIw4EBAAAw5Nhs0p//rIonXtWNk5do9mzzgjpuMF/b\nwHri15JPPZX6WnPjRnOcm3gYNHGi2v1Pw5oGJOzprI9hpispMcP9LVvM0MPlMpc7ndLpp5stevx+\n6cEHzW3XrGl7m/hky+RQpyN+v9l1K9ULx4ChZg8AAAAGks+n3xfdp/PXXaeXl8bahDqD+doG1tXR\ntWZiT8ScHLM3ytKl0qRJBD5WNyDduGpqjIztptRTiV2uEuvZDIV+kWlpsmoY0rBhZuAzYsQAHBAA\nAACQZs8y9KuXj9I/J8zX9avOHhQlGIBkiTVZGxvN1j1xQ+Ea1urSXrNnMBdo7sz48dK2bWaXrTVr\nzILMVpZYYHrAijXv2mV24aqp6X4BJQAAAKCXamul+7/5rH7WdLMcH63isygGvdpas0XPjh1my7S6\nOqmiwuy1smqV9a9nrahfa/a8/PLLOuiggzRx4kT9/ve/7+3uBpX4k6G+3hwty+oSmwDu2DFAfZTj\nXbh4cwUAAMAA8vul+f/+lhyRoPTKK+k+HaDXkkt0VFSYgU9VlXTAAdTysZpehT3RaFRXXnmlXn75\nZa1fv15PPPGENmzY0FfnlvFSFWG2srQUa/78c+r1AAAAID3sdun666X//d90nwnQJxJr+zid5jKb\nTQqFzFo+o0aZP2Vl6T1P9F6vwp73339f+++/v/bZZx85nU6de+65evbZZ/vq3DJeR6NrWVVaijWX\nljISFwAAANJn7lypvFz697/TfSZAn1q1Sho3Tjr+eHPebpciEbOlz377ScOHSyefTGufwapXYU95\nebnGjx+/e37cuHEqLy/v9UkNFoNxdK3eGvDfmZY9AAAASCeHQ7r2Wlr3wHKKiqStW6UlS8wv9IcN\nM5fbbFIs1jpc+4CU70Cf61XYY6OOCvobYQ8AAADS7eKLVfPmGl0+7T/UNYHlxL/Q//DDti19JGnq\n1KFRssSKHL2589ixY7V169bd81u3btW4cePabbdgwYLd08XFxSouLu7NYZEhBmIo9rqPSvW9+RMU\nGDaAw70DAAAAidxuLSr4uU75z62aq6c0adIAjUwLDKB4S5/E4doXLuT/PJMsX75cy5cv79a2vRp6\nPRKJ6MADD9Rrr72mwsJCHXnkkXriiSc0adKk1gNYeOj1oa7fh2IPhRTOzpXLCEqyqaTETJwBAACA\ngXbmKU16YFmRpulDfaEiPpsCSLt+G3rd4XDo3nvv1amnnqqDDz5Y3/nOd9oEPbC2fh+KvaZGTU6/\nJNuQGfEMAAAAmWnh4hy94jlTc7WYz6YAMl6vWvZ06wC07LGs2lpp0iQz6Bk5UjrwQHM4+j7rbvXf\n/yr67Tn67pT/6v77aT4IAACA9Gp87nVVXHyNhm/+kM+mANKu31r2YGhLHIr9wAOlt9+Wli7twxY+\nNTXKGjFsyI14BgAAgMyU+82Z2i97u/wVG9N9KgDQKcIe9Eq8crvPZ873aZPW6urW8f8AAACAdMvK\nMr/pfPLJdJ8JAHRqQMKe4mIxRKHFLVpkvu8tW9aHrXBqagh7AAAAkFm++13piSckSlUAyGADEvas\nWNHH3XuQceItfPq0uxVhDwAAADLNUUdJgYC0Zk26zwQAOjRg3bioWI8eI+wBAABAprHZpHPPpSsX\nBr1586QxY6Thw6WxY6XjjqNHjpUMSNjT5917MDTU1JivPAAAAEAmiYc9dOXCIDRvnllq5R//MEdW\nrqmRtm9PPeBOfFtCoMHHMRAHWbx4II4Cy6mulg4/PN1nAQAAALR1+OGS2y29957ZrQtIk3nzpI0b\nJa/XrKM6f37beb+//TYbN5qlVhL5fFJ9vWS3S88+a/5bl5eby+rrW4/Ftf3gMSBhD7BH6MYFAACA\nTGSzmYWan3ySsAcpzZsnPf+8FAxK06ZJTz3VeU+X5EAmeduO1icGN/PmSRUVbecXL26/jddrTk+d\nanbjcjqlu++W9t9fikSkUMjMMRMNG0ZZlsGGodeRuQh7AAAAkKnOPde8ko5G030myEAbN7Z2kXr1\n1a4HK4oHMh0NbNTR+nhwE6+Rmzyfapv4SMqvvy69+KK0ZIlUVNQaINm/Sgny883bYcOkjz6iLMtg\nQ9iDzEXYAwAAgAyzu4bJ1QcqMqpAevPNdJ8SMlA8YJGkKVO6bhWTKqTpzvp4cBOvkZs8n2qbjkZS\nXrVKGjfODHZKSqSPPzZvN20ywyAMLjbD6N+qYjabTf18CFhVYaH0wQdmaXgAAAAgAxQXt3aJuXXY\nbTo4u1R/mfLXlF1vMHTV1kqXXmrW8H744a7/N2przSDx/vtTb9vVegxNneUthD3IXB6PtGtX21gc\nAAAASKPZs82uNNOnS9OC7+jitT/XMXpXJSUUr7WqrurpWFlPaw9hYHWWt9CNC5mppUWKxczABwAA\nAMgQiV1iQsMLVKAdHXa9QWaKd8UbP1467rj2w4onDzfeVT0dK+tp7SFkDlr2IDN9+aVZHn7HjnSf\nCQAAAJBSbXmTvHuPVHNVs/zDbOk+HaSQqlVOYle8uMSWWYnrS0qkxsbW1lyJtXCGgnhLNknKy5PW\nrqV+TyahZQ8GH4ozAwAAIMP5x+bIleOU316f7lNBB1K1yolXiYiPNpXcMquj0auGWtAjmb/78OHm\ndEODdM016T0fdB9hDzJTdTVhDwAAADJfQQGt0XsguYtUcpeqjrpW9XS/calGsYqHN/HRppJDnO6O\nXjUU+P3SjBnmNN0VB5cB6cY1a5Yx5ApZoZeef17661+lF15I95kAAAAAu7XrFnTGTOnmm6WZM9N9\nahkr8TGrr5fefttcXlIiVVS071IVV1JiXkMmd8PqqmtWYpcsRrHqPR7DzJX2blxDsZAVeoluXAAA\nAMhA7boFFRSY9SaxW2cFjktLzW3irUSSu1Qld61K1Q2rs65Zya1PhnKrnL4Sb900Z07PW1whfQYk\n7KG5F3qMsAcAAAAZqF2oQDcuSW0DnvXr24YxiY/ZypVtu0gld6lK7lqVKsTprGvWUKyrMxCG8ohk\ng9WAdOOqqTF4wqFnFiyQDEP69a/TfSYAAADAbu26tNx6q1RXJ/3ud+k+tbRK7EYVz7/io1dJe94N\nKFUXIroVDbz4qFxDcUSyTNZZNy6GXkdm+slPpP32k666Kt1nAgAAAHRs4UIz5Xj44XSfyYBLrJ0T\nDkuvvmqGAf/4hzlqE2GMdRCwZabO8hbHAJ8L0D01Na1j/AEAAACZagh344p37ZHMei4lJa1hQLxA\nMqyBv+ngQ9iDzETNHgAAAAwGQzjsSayds3AhLT6ATDIgBZqBHiPsAQAAwGAwhMKe5FG2KIoMZC5a\n9iAzVVcT9gAAACDzjRol7dolRaNSVla6z6ZfJXbbmjfP7NZD1x4gMxH2IDPRsgcAAACDgcNh1pqs\nrDRb+VhMYhFmp9NcljjkOYDMRDcuZB7DIOwBAADA4FFQIH35ZbrPol/EW/MsXSrl5tJtCxgsaNmD\nzBMISHa75PGk+0wAAACAro0ZY9m6PRRhBgYnwh5kHlr1AAAAYDCxWJHmxK5b990nXXNN65DqAAaH\nAenGFa/WDnQLxZkBAAAwmFgs7EnsunXNNWYRZoIeYHAZkLBn6VIzHQa6hZY9AAAAGEwsFvYkdt2i\nEDMwOA1INy5eJNAjhD0AAADIcIldnZ46u0A5O95N9yn1Cl23AGsZkLCHau3okZoac/hKAAAAIEPF\nuzpJ0u3NBVpgDO6WPYm/T7zrFoDBa0C6cRH0oEdo2QMAAIAMl9jV6We3Df5uXHTdAqxlQMIeoCde\neLRaC5cMo7A3AAAAMtaiRVJJidmLwXfA4Ax75s2TiovNAXXuu6/19+HLemDwY+h1ZJyWigatKS/S\n0jLzDYgmpAAAAINDYt2XRYusHRr4/QmfU418KRiUmptbm8gMAnTdAqyLsAcZJ9fWpCbl0IQUAAAg\nUTQqVVdLVVW6fX6l6jdVaZStSpefWaW3llTJqKjU8FiVpoyvUu1nVXK2NEjRiGIOlxpjXkVcXkWc\nXu17iFf/3epVdYtXUadHx5zs1VsfelRbKzXUxpSfF5XXEZbXHVFLQ0SurIiOPiKi9R9HFKwPymfU\n6oDRtcqqr1Wosk6KRWWT5HDZ9MewTdGYzTzf4TYFbDbFDMlms0k2m2S3KRaTZLPJljCdk2uT3SY1\nNNsVjDkViLokp0tRh0vj9nPJ4XFJLpc2lLpU2+JWxOXVjJkeufxeLXvbqx31HsXcXs292CPPCK+u\n/bVX2+tz1ejw68+P5+veR/P1ydZ8rf0iX4X7uOTz9XEYZbO1jsi13359tNP+R9ctwLoIe5BxTpzR\npI2FObrtFWt/GwQAAIaQSEQKBHT1FSH9d7Nb9lyvHn/C3vpZJxiUysulbdv04IKtim3ZqsLoVp16\n8FY5d25Tw4at8gRr1eTwK3ffkTp1x0iV1o9UpUbp5ehIbQ+N03sVU1SlkTr88JHacvBIvfp+niJy\nyBkOy6tmeQPmzzfdzQoMa9bna5vlVbM2rmtWqCWgz8ulmOyKNDkUkUPZeU7VNJjTqyodqsl16D9l\nLtXKrxkz/LrrIb++/Z18vfGWQzYZOvubhpobDb3yL2naVEPZbkPvrpRsMmQzDEnSKL+hXbsM2WRo\n5Ajtnj7j69JDfzN04bdj+uDdsJwKyxUNyaWQvjk6pN//JiSFQnrwxyFtXBOQRwGt2RTQFZc2a+Oy\ngL78olleNen1v1fpmyc062u7mnV0uEH5qlPVWXW6IqtW7pY65atOoXKX6pSvpr3z5Z+Ur/Xl+doZ\n8ivozlfxnHxlj87Xk0vz9XnNCDX4xuqGP4+Vb9LYrlvsDMKwZ9EiszUWo24B1kPYg4zjCjXqJ9fn\nSrzhAACA/mYYUnOzjj+sVqGKWo3IqtXCu2o12lWrx++rV8VOQ063Xd+7zK5/PG3Xxx9LsUhU+46P\n6rLvReV1RvTU/0UV+LJOI4wqjbZVyllXKW9TpUbYquWJNctjC8gWi0kejxYE3XJEWuRRQMHh2aqW\nV1HZla86OcaNkb1ovPb9dJw+rByvtTpIG8adrGseGK+LfjxOz707UrFwlkoOlxonSEuXmi0yli2T\nzjtPWrrJnL/2H+Z8laT8fKmyzrwtqzPXX/3MV9uvNefvjd+/1Nyu7qvt/H7p1VfN6b+89NU268z5\nXz0pyS/Z86SQzGV/+pv5kMbDg/POk5rUfp+bXm2d/vyr6Tv/bu4v5Je2J93nuqe0+3PhhrHS0jVf\ntUT5l7n8xedbf/cfLTOXXfm0VFVl5jPr10uX/dB8vPJ9hsL1zZp5WK2e/GudZNTp4e/XqWxtnfyq\nVdWKOl3wrTq5y3Zq/+1VGqtyBWaWyxfZribDo+3RAq1wnKhNk07X/JeK5R/jaf1fGjMm4+v2pOpm\nR9ctwJpshvFVzN5fB7DZ1M+HgNV84xvSdddJJ52U7jMBAAADJRrVjy8LaOunzfK7A7rnd8265/cB\n7dzcLJ8zoOuualauPaC/39es6u0tynW06IKSoF59sUUNlS1qrglqZE6LchwtmjkjKFesRZ+salGw\nMSSnPSq3I6ZQS0zBlphyXGH5YnUa462Vva5Wcjj0ZYtfNYZftfIrkD1M3zjLr+feyNO2L+2yK6b9\n940pGIhpxw5DUWUpqiztOyFLp53u0P/9M0vrt/lUqVEK5Y1UacMoVWqUqjVcTcrR7LM9euIpp2Sz\nafZsM3Q4YlpMec4WrVnZpCxFValROrskS4sXa/c28SDH72+/TGrbIqO2NvX87bebtVjit93dPt6l\np7NjSKmXxXW1z+T9d3SfxP129xzKyqTjjpPeeksqKup8v915vP35hr59XLW+eGerTtYyna4XdITj\nI31WWKxFvh9o7fjZeqbgh3JPP1T60Y/67nnRx4qLW2v0lJQQ9ACDXWd5y4CEPTNnGkOiSBv6yFFH\nSXfdJR19dLrPBAAAk2FIsZjZFSca3X37s59EtKU0qpzsqO69O6r8vJi5Pho1t49G9eubotpaFpPX\nHdUtN0d17z0xlX8Rldcd1U03xpTrabt9m+mvbh+8P6qdX8bkcUb1g8uj8mabyxc9GlVlRUzZjqgu\nOj8qj9tcvuSfUe2qMpefPSeqbFesw2OseD2q+rqYXFlRnfD1qFyOFOeRMP3JmpiqKmIyYoZ8PkOH\nHWrImWWYj9FXj9PGjYaCzVG5bBFNKApr59awIsGInAqrYERE9mjYfAzDYQUaI8qKBOUwwgraPWqM\neRWQR1l5XjUbHlU0mvP+MV5N/7pHS1d4tWVntlqUrX0OylZtIFuflrnVInNZUG5NmZGtn16brWtv\ncuvDT1yKyCFfvl3VdXazm5IcqlO+jv+WXw885Zfcbo0a1bYlSFFR+wv+884z5yVpyhTpjTfaBwPx\n1jCJLVMSRzdKDCY62l9PQxX0Tncf7/jfWTL/XsufrtbfTl2ssz77nfb9/+3deVxVdf7H8fdlExAQ\n3M0FFzB1XBM1ywwfZRlNaSmj+ZvqZyX+nJlmppqWsd12W2ZanalJnWows8alBX8uP7HclcwoHUFF\nBQMU5cqqbOf3xwkEBLzAhbu9no/HfZzrveee8wU55577vp/v96sjWjboaf1qark0f75jfhAb1BVs\nAXBdDg97JHMXzpwe1zdzQGMfr297Dz1k3+27tcGDpaVLpSFDHN0SAEBLKi+Xzp41b8XFNZd1PdbA\nc9s2nlXxmRK18SrV6BHmeB/7kst0rqBMvl5lGhBRLh+VXRDWVC5PnyyTUVomb5UrJLBMXhUXriMv\nL8nbW/LxqVpaC310rtRL5fKWX4C3Onb2Pr+et3n/8DFv5RV6q0JeCgnzVkmZt6z55mvad/LWLwbX\nXL/G8uf76xO9lXnC3Eav3t6acK35+MovvJX+k/l4v0hv/XKy+fhHS72Vlm4+PuAX3pp+W/3bfu0N\nb/0n1Vz3sihv/ebeC9epfv/hP3tp7w9eMkdaseiq8RY9/njl4LteksWiP/zRoqS93iqVr6Kv9dHJ\nXF9tS/JVmXx0XYyv3v67j+TrK/n4KGayr/5vi5/OqY26drUoK+vCcKWhiovKdeoKVxoTwtSuBJHq\nrn6ZNcvMtJYsqTsYkBquTKmuvu3BOdX1/9W7R5kOHG+rniF52v/wEnU4kuTUIx0TGgLuxSnCHmdJ\nj+sLUeoraWzs45VqP3/ihH22f7Fw6NAh8wLF7jMMtKY+faQNG1xqcDsAcGkVFWZwUlRkhie1bm+8\nVKyT6WcV7HtWv7u7WIFeTQ9latwvK1OJl7+KLQEq9fZX2CUB8g70l/z9pYCAmsu6Hqv23At/8df3\nB/xUKl+NvsJXDz3qqz894qOkZF+Vy1vjJ/jo2Rd+DmqqhTWVy9jbfLRlh7fK5KNf3uytRR9UW6dy\nPe5pNvIAACAASURBVIvlgl+dLd+S1xdONObaqL79NPbxxmz7YutLNatRmvozV1/3008b7mok2db9\nqLkhDGCrceOkxVsidZM+111XHNBDHd6XVq92dLNq8OgvkQE35/CwJzbWaLU31ItV1EyZUneIYu+L\nKFsvchq7fVvCoUqxsebrPv/cnOBh5Ehp+XIXOMF37iz98IO5BABPVVIiFRRIhYV64oFCZR4qUju/\nYj31cLGCvIv1978W6/TxYgX7FOvumebMNHUFNTbdSkqkNm3M8KSO287kAB3PDVCxAtStt78mxDQQ\nwDQmqPH1VfQEi13Gj7BpzA07Bh6VbPmWvL5wojHXRvW9prGPN/VnqL3+xapRGvMzU2kAVxYTI92b\ncIPWRvxW8xd2UvC8e6WdOx3drBoYpwdwXw4Pe1pzgOaLVdQUFNR9MWfviyhbL3Ia+/jFwqHapcnV\nw63K30HtKqHKaqCjR2tWBVUPylJTpZMnzYrr3bvPlze3iMBAc2dt27bgTgDATsrLpfx885aXV7X8\n28v5sqbnK8S7ULN+VaiAikKpsNbt5zCnzpvFYp4H27bVsdNtdarYHLckuHOAhowKUOKOAB3LMQOY\nHpEBunFq3UGNTTd//zqrViq15BgP9tp2c8c4IXAA0BRWq7Qq/F5ZO/TTnvBbtCj1KnllHHN0s2pg\nnB7Afbll2FNfOeLFKmoqX+uqF3MXC4dqlyY3VGpdVzVQpdpBmY+POWSBZP7OR42qPyRq1u+1ouL8\nzry8mrEhALiI0lLz5Gm16vkHc3Xq8Bm198nTfffkK7AsTys/zFdRdp5CLPm6bkye/M7VDHOqlmfP\nSsHB5i0kpGq5aU+wjuQEqVBt1aN/W908M6gqvKnzFlTreV/fqqY2t2qluVoyCCFkAeDq3oh4Q16H\nUvQnvaICS4h8ys81GKC3Ns6zgPtyy7CnvnJEe5RKu5OGSq1rVwPVrgqqHpQdPiydPm0GPUOGSDt2\n1L2/ZpeGFhRIXbuaSwBoiGHo3lkFyvqPVZ18cvXSn60KLss1T3y5NizPnjVPimFh+k92qDLy2+mM\n2qlDeLCibw7Rh6uC9cOxEOUrWANHh+jeeTXDnKplYGCTx3KxFTPzAIDzeiLqK12R9IYej1qjHalh\n8jp0UOrQwdHNAuAB3CLsqV3J05TBDVFT7Wqg2lVB1T9InDlzfoaKuXMbDoma9X+RlSUNGyZlZ9vt\n5wTgvObeU6qf9lnV2TdXrz1hW1iTnWKVf1Gu2pafUYmljU5XhCpXYfLpGKqBY8OqApwLlrUfCwqq\nCmlaonKGMAYAPEPe7hQVRcfIP+OgQscONEca/8UvHNYeBmQGPIdbhD21K3nefZeLaEe5WEjULIcO\nSRMnmqVEAFxDebkeuDNHuf/JVkfvXD35+1y1La0Z0GxLsMo4nasQw6qBXXLlnW8+Xl50TrkywxpL\nWKgiohoIa35e/vp3ofrfXWFmFU5XvxrTNDtqvBcAgAcrKTErPfPzpeuukx57TLrmGoc1p1s38/tT\nSZo8WVq50mFNAdDCGspbfFqrEY1JmOtaNzDQfC4q6vyFNyPJO0b1333tZbMVFprftgNwqLg4Ke0/\n59TdO0tvzstUcGGWlJmpL97LlDIz1bk8U5ddkimfE5nSyZN63BKq46WddVrttf/BMEVdWy2g6dtX\nm7xDte10mKwK1aioML3yD/P5m34VpIQ1lvPjqtkQqpzuKOWo7mmam6qu9xTeZwAANvHzky65RDpy\npGbS4iDnzp2/70RDBwFoZa0W9qSknK/MiYur/wK68rkzZ2r+Oz6eb1g9QkEBs3ABLS0/X8rMrLp9\n8nqmyo5nqUt5psb3z5TvyUy9fCBTAWX5OqHOyt7bTcHjuknduinndFdtzxqpTHXToKHd9MLn3aQu\nXTRzil/NwfBrnae/XislHDCff+yT88/HL238ub32+wGBDADA4SIipIMHzbEnHRz2jBwprV8vjRgh\nLV7s0KYAcKBWC3tqV+bUJyXlfNATFnZ+XS7oPQSVPUDTGYY5knpGhpSRoY9eSJeRnqGu5RmK7peu\nU3sz1C7/uCwWQ769usn7kq5St27yTu+mPce7aa0Ganu/bnr0X131P/d307L/66iRUV41wptPYqSE\no+a5/J+fnn/8YoF8fc835dzO+wEAwOk4UdizfDlfkgNoxbCnoQ8C1bttVc40GxYm7dnDCcrjFBZS\n2QPUxTCkU6c0f06GilMy1L0iXfdMypB/ToaUnq7spAy1K8hQqVcbBUb2kHd4D7U73FO7Mntoo8Zr\nR98eSonsqZW7uytfwYqNslQFJu/HSAnHzQDnr6slhUoLP5PK6zhnNzW0IaABALi1yrBn5EgpObnV\nd197GAzecwG0WthT/UK/9smoehevKVPOD8BM0OO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AoF/sOQGdqcd1jW6Wq2KbysuzfUZAZiQaEzz6qHlNl59vzvf7pdWr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\nl9q3V9N90frhYJSydKVWdQ7Tns5h+uynMOWrpRL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Vh3v2LF68WLfddptCoZC+973v6Z577ml+AMNQSYnZod4c3aV3SrTS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\nXmnAAN38px5astz7dHjz/nhpd7o5R3L37tKcOXX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lZLhBtUPL2noNbTP72vPPSy+/zAhmAADYUN29v9TqJ7crv+RdXVX7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\nXXrqKbp0AAC4cHLkllL37uYw6OBgcxn2zp3dXSa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"text": [ "" ] } ], "prompt_number": 11 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "## FLAPS DATA SELECTION" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print w_norm_loop_2" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 2.81497583e-02 2.96322890e-02 3.22782793e-02 3.33181348e-02\n", " 3.48861823e-02 3.72418467e-02 3.77506357e-02 3.94264775e-02\n", " 4.09797931e-02 4.15245136e-02 4.36358442e-02 4.39426986e-02\n", " 4.57917299e-02 4.71923369e-02 4.80884668e-02 5.06169613e-02\n", " 5.15892657e-02 5.32554060e-02 5.59437954e-02 5.63120926e-02\n", " 5.86954242e-02 6.03206026e-02 6.15275393e-02 6.38192457e-02\n", " 6.44469678e-02 6.70088786e-02 6.74616145e-02 7.03052277e-02\n", " 7.06095669e-02 7.39418475e-02 7.40967120e-02 7.74009660e-02\n", " 7.88892458e-02 8.08493051e-02 8.40461242e-02 8.49030888e-02\n", " 8.88135061e-02 9.24080861e-02 9.60723730e-02 9.98070856e-02\n", " 1.03624440e-01 1.05450116e-01 1.07514736e-01 1.11613793e-01\n", " 1.13891487e-01 1.16132889e-01 1.20886258e-01 1.22979982e-01\n", " 1.25706459e-01 1.30594929e-01 1.34931493e-01 1.35600177e-01\n", " 1.40744120e-01 1.42350974e-01 1.49325623e-01 1.49848785e-01\n", " 1.56729653e-01 1.58523350e-01 1.63922766e-01 1.68756261e-01\n", " 1.70955624e-01 1.77699594e-01 1.83912497e-01 1.90214510e-01\n", " 1.93323297e-01 1.98217517e-01 2.01282827e-01 2.09423451e-01\n", " 2.13420104e-01 2.17838232e-01 2.26529685e-01 2.29184658e-01\n", " 2.35351569e-01 2.43769943e-01 2.45861512e-01 2.50742437e-01\n", " 2.53403518e-01 2.60990080e-01 2.62397155e-01 2.70954583e-01\n", " 2.71853588e-01 2.81310739e-01 2.86566070e-01 2.91813855e-01\n", " 3.02343560e-01 3.13218206e-01 3.24437076e-01 3.35864348e-01\n", " 3.47212211e-01 3.59507587e-01 3.72787483e-01 3.85866882e-01\n", " 3.99473395e-01 4.13754700e-01 4.28370527e-01 4.44439780e-01\n", " 4.59556131e-01 4.69585311e-01 4.87148234e-01 5.00078517e-01\n", " 5.02143497e-01 5.20191134e-01 5.23918301e-01 5.39830893e-01\n", " 5.41737055e-01 5.56737708e-01 5.71163457e-01 5.85433983e-01\n", " 5.99634083e-01 6.13772022e-01 6.28130580e-01 6.43367302e-01\n", " 6.47996707e-01 6.58975914e-01 6.74001718e-01 6.75884525e-01\n", " 6.87834958e-01 6.99026879e-01 7.01161565e-01 7.12040883e-01\n", " 7.22415364e-01 7.27235925e-01 7.35383376e-01 7.36769610e-01\n", " 7.44632484e-01 7.49388009e-01 7.51232728e-01 7.55674931e-01\n", " 7.59707874e-01 7.59772191e-01 7.63235981e-01 7.66051568e-01\n", " 7.66471247e-01 7.69231140e-01 7.69598718e-01 7.71677711e-01\n", " 7.73930611e-01 7.75926961e-01 7.77698381e-01 7.79305953e-01\n", " 7.80767284e-01 7.82090279e-01 7.83334584e-01 7.84554097e-01\n", " 7.85771813e-01 7.86992404e-01 7.88219103e-01 7.89440771e-01\n", " 7.90653098e-01 7.91862190e-01 7.91887701e-01 7.91900277e-01\n", " 7.91908542e-01 7.91989028e-01 7.92146408e-01 7.92208928e-01\n", " 7.92598066e-01 7.93159674e-01 7.93881536e-01 7.94778744e-01\n", " 7.96090960e-01 7.97645353e-01 7.98867022e-01 7.98952179e-01\n", " 8.00685152e-01 8.03279401e-01 8.06113672e-01 8.06810742e-01\n", " 8.10847997e-01 8.12337355e-01 8.16062007e-01 8.18616012e-01\n", " 8.21802052e-01 8.26781070e-01 8.27856138e-01 8.34384879e-01\n", " 8.38909723e-01 8.41892751e-01 8.50237825e-01 8.58113994e-01\n", " 8.59707193e-01 8.69650497e-01 8.77741895e-01 8.81052617e-01\n", " 8.84893327e-01 8.88904712e-01 8.97597602e-01 9.00953238e-01\n", " 9.08561358e-01 9.16561491e-01 9.24450954e-01 9.27497580e-01\n", " 9.31506090e-01 9.38199756e-01 9.38702436e-01 9.43447900e-01\n", " 9.47508151e-01 9.51587446e-01 9.55690097e-01 9.58129841e-01\n", " 9.59862813e-01 9.59747114e-01 9.55262153e-01 9.50851930e-01\n", " 9.46442785e-01 9.42023938e-01 9.40469903e-01 9.37474660e-01\n", " 9.32328201e-01 9.32150700e-01 9.26382628e-01 9.19396480e-01\n", " 9.17157234e-01 9.11593252e-01 9.03142899e-01 8.94064105e-01\n", " 8.82966684e-01 8.81822627e-01 8.69734936e-01 8.59169300e-01\n", " 8.52415988e-01 8.48504493e-01 8.40016771e-01 8.33244056e-01\n", " 8.27604259e-01 8.21909846e-01 8.15607474e-01 8.11207671e-01\n", " 8.09185091e-01 8.02842116e-01 7.96561662e-01 7.90246713e-01\n", " 7.83841218e-01 7.83800256e-01 7.77153301e-01 7.70301537e-01\n", " 7.63243527e-01 7.61043445e-01 7.55950166e-01 7.47864157e-01\n", " 7.41943376e-01 7.36675829e-01 7.30558863e-01 7.20552320e-01\n", " 7.11218413e-01 6.95362592e-01 6.94740260e-01 6.79351548e-01\n", " 6.68176515e-01 6.64247773e-01 6.51752619e-01 6.40519017e-01\n", " 6.27184865e-01 6.13955991e-01 6.12775284e-01 5.96678364e-01\n", " 5.88289813e-01 5.79785563e-01 5.65199559e-01 5.54960180e-01\n", " 5.48754463e-01 5.34016469e-01 5.26623938e-01 5.18864186e-01\n", " 5.03058311e-01 4.92256605e-01 4.87039002e-01 4.70945675e-01\n", " 4.55976283e-01 4.41170379e-01 4.38393599e-01 4.26247339e-01\n", " 4.12038974e-01 3.97005624e-01 3.87863232e-01 3.80675509e-01\n", " 3.64554156e-01 3.63700066e-01 3.49383548e-01 3.41465698e-01\n", " 3.35192790e-01 3.25678866e-01 3.21323259e-01 3.13455354e-01\n", " 3.07169151e-01 3.02866721e-01 2.93228835e-01 2.92796220e-01\n", " 2.84221544e-01 2.81112398e-01 2.75397864e-01 2.70990156e-01\n", " 2.66181812e-01 2.61494557e-01 2.55541438e-01 2.52299346e-01\n", " 2.43165577e-01 2.40259083e-01 2.33970006e-01 2.24886900e-01\n", " 2.23955198e-01 2.16057470e-01 2.08306702e-01 2.07497886e-01\n", " 1.99119755e-01 1.94961051e-01 1.91575592e-01 1.85731705e-01\n", " 1.84185576e-01 1.77548682e-01 1.76314796e-01 1.71649819e-01\n", " 1.69145758e-01 1.66352449e-01 1.62917764e-01 1.61623514e-01\n", " 1.57391151e-01 1.53532834e-01 1.49928553e-01 1.46440689e-01\n", " 1.44369601e-01 1.42986601e-01 1.39538621e-01 1.36091000e-01\n", " 1.33914993e-01 1.32643380e-01 1.29196119e-01 1.25749217e-01\n", " 1.25541893e-01 1.22303393e-01 1.18857210e-01 1.17202568e-01\n", " 1.15412105e-01 1.11966999e-01 1.08865758e-01 1.08522613e-01\n", " 1.05093677e-01 1.01693486e-01 1.00598655e-01 9.83080265e-02\n", " 9.49258012e-02 9.31594122e-02 9.15327965e-02 8.80452921e-02\n", " 8.73726558e-02 8.43145320e-02 8.14734340e-02 8.01676860e-02\n", " 7.82018776e-02 7.45523225e-02 7.14679687e-02 7.10590691e-02\n", " 6.78004479e-02 6.39299145e-02 6.37412745e-02 6.00874077e-02\n", " 5.82311901e-02 5.69505940e-02 5.40635038e-02 5.13636164e-02\n", " 5.12989398e-02 4.85638395e-02 4.74061289e-02 4.58714977e-02\n", " 4.32596422e-02 4.31572376e-02 4.07451609e-02 3.89295459e-02\n", " 3.83715308e-02 3.61822289e-02 3.48046180e-02 3.41337782e-02\n", " 3.21280859e-02 3.06671141e-02 3.00702931e-02 2.78619476e-02\n", " 2.61559234e-02 2.54595723e-02 2.36421607e-02 2.18804428e-02\n", " 2.01654358e-02 1.89437672e-02 1.84849229e-02 1.67806953e-02\n", " 1.49719072e-02 1.30682602e-02 1.13065423e-02 1.11631759e-02\n", " 9.67130304e-03 7.90850720e-03 5.95922726e-03 5.19496580e-03\n", " 4.23128491e-03 2.82959992e-03 2.03335949e-03 1.74950121e-03\n", " 9.15532774e-04 3.09010277e-04 0.00000000e+00 3.12603420e-05\n", " 4.51658044e-04 1.35425550e-03 2.81486803e-03 4.71096947e-03\n", " 6.54922130e-03 6.96171409e-03 9.54805824e-03 1.03683727e-02\n", " 1.18562931e-02 1.43175959e-02 1.45583365e-02 1.68683680e-02\n", " 1.90393448e-02 1.94518376e-02 2.20320734e-02 2.35961685e-02\n", " 2.45943435e-02 2.71145739e-02 2.81497583e-02]\n" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "from zutil.post import get_case_root\n", "# normals \n", "f_nx_1 = 0.003629 ; f_ny_1 = 0.996368 ; f_nz_1 = 0.085077 ; \n", "f_nx_2 = 0.003626 ; f_ny_2 = 0.996367 ; f_nz_2 = 0.085081 ; \n", "f_nx_4 = 0.000246 ; f_ny_4 = 0.989663 ; f_nz_4 = 0.143413 ;\n", "f_nx_5 = 0.000241 ; f_ny_5 = 0.989664 ; f_nz_5 = 0.143404 ;\n", "f_nx_6 = 0.000247 ; f_ny_6 = 0.989663 ; f_nz_6 = 0.143415 ; \n", "f_nx_7 = 0.000229 ; f_ny_7 = 0.989666 ; f_nz_7 = 0.143389 ;\n", "f_nx_8 = 0.000244 ; f_ny_8 = 0.990009 ; f_nz_8 = 0.141001 ;\n", "f_nx_9 = 0.000193 ; f_ny_9 = 0.990019 ; f_nz_9 = 0.140931 ;\n", "f_nx_10 = 0.000301 ; f_ny_10 = 0.989998 ; f_nz_10 = 0.141080 ; \n", "f_nx_11 = 0.000256 ; f_ny_11 = 0.990007 ; f_nz_11 = 0.141018 ;\n", "# points which define the cutting planes nebdeac\n", "f_x_loc_1 = 1633.0167311083 ; f_y_loc_1 = 213.9810000000 ; f_z_loc_1 = -110.2460000000;\n", "f_x_loc_2 = 1652.6042432698 ; f_y_loc_2 = 407.4420000000 ; f_z_loc_2 = -91.5960000000;\n", "f_x_loc_4 = 1668.8232828954 ; f_y_loc_4 = 633.7100000000 ; f_z_loc_4 = -67.7010000000;\n", "f_x_loc_5 = 1719.6203614083 ; f_y_loc_5 = 764.5860000000 ; f_z_loc_5 = -54.8240000000;\n", "f_x_loc_6 = 1793.8206003685 ; f_y_loc_6 = 956.6800000000 ; f_z_loc_6 = -34.7680000000;\n", "f_x_loc_7 = 1809.4990162299 ; f_y_loc_7 = 1004.9670000000 ; f_z_loc_7 = -30.0750000000;\n", "f_x_loc_8 = 1829.0543532948 ; f_y_loc_8 = 1053.2090000000 ; f_z_loc_8 = -21.8780000000;\n", "f_x_loc_9 = 1855.0727180180 ; f_y_loc_9 = 1147.3930000000 ; f_z_loc_9 = -12.1070000000;\n", "f_x_loc_10 = 1916.7026744926 ; f_y_loc_10 = 1249.1220000000 ; f_z_loc_10 = -2.459000000 ;\n", "f_x_loc_11 = 1949.5082093248 ; f_y_loc_11 = 1351.2150000000 ; f_z_loc_11 = 6.5750000000 ;\n", "\n", "\n", "\n", "f_fig1 = pl.figure(figsize=(20, 10),dpi=600, facecolor='w', edgecolor='k')\n", "f_fig2 = pl.figure(figsize=(20, 10),dpi=600, facecolor='w', edgecolor='k')\n", "f_fig4 = pl.figure(figsize=(20, 10),dpi=600, facecolor='w', edgecolor='k')\n", "f_fig5 = pl.figure(figsize=(20, 10),dpi=600, facecolor='w', edgecolor='k')\n", "f_fig6 = pl.figure(figsize=(20, 10),dpi=600, facecolor='w', edgecolor='k')\n", "f_fig7 = pl.figure(figsize=(20, 10),dpi=600, facecolor='w', edgecolor='k')\n", "f_fig8 = pl.figure(figsize=(20, 10),dpi=600, facecolor='w', edgecolor='k')\n", "f_fig9 = pl.figure(figsize=(20, 10),dpi=600, facecolor='w', edgecolor='k')\n", "f_fig10 = pl.figure(figsize=(20, 10),dpi=600, facecolor='w', edgecolor='k')\n", "f_fig11 = pl.figure(figsize=(20, 10),dpi=600, facecolor='w', edgecolor='k')\n", "\n", "\n", "f_fig1.suptitle('PS1', fontsize=30, fontweight='bold')\n", "f_fig2.suptitle('PS2', fontsize=30, fontweight='bold')\n", "f_fig4.suptitle('PS4', fontsize=30, fontweight='bold')\n", "f_fig5.suptitle('PS5', fontsize=30, fontweight='bold')\n", "f_fig6.suptitle('PS6', fontsize=30, fontweight='bold')\n", "f_fig7.suptitle('PS7', fontsize=30, fontweight='bold')\n", "f_fig8.suptitle('PS8', fontsize=30, fontweight='bold')\n", "f_fig9.suptitle('PS9', fontsize=30, fontweight='bold')\n", "f_fig10.suptitle('PS10', fontsize=30, fontweight='bold')\n", "f_fig11.suptitle('PS11', fontsize=30, fontweight='bold')\n", "\n", "f_ax =f_fig1.add_subplot(2,1,1)\n", "f_ax2 = f_fig2.add_subplot(2,1,1)\n", "f_ax4 = f_fig4.add_subplot(2,1,1)\n", "f_ax5 = f_fig5.add_subplot(2,1,1)\n", "f_ax6 = f_fig6.add_subplot(2,1,1)\n", "f_ax7 = f_fig7.add_subplot(2,1,1)\n", "f_ax8 = f_fig8.add_subplot(2,1,1)\n", "f_ax9 = f_fig9.add_subplot(2,1,1)\n", "f_ax10 = f_fig10.add_subplot(2,1,1)\n", "f_ax11 = f_fig11.add_subplot(2,1,1)\n", "\n", "f_ax.grid(True)\n", "f_ax.set_xlabel('x/c')\n", "f_ax.set_ylabel('Cp []')\n", "f_ax.axis([0,1.1,-5,5])\n", "f_ax2.axis([0,1.1,-5,5])\n", "f_ax4.axis([0,1.1,-5,5])\n", "f_ax5.axis([0,1.1,-5,5])\n", "f_ax6.axis([0,1.1,-5,5])\n", "f_ax7.axis([0,1.1,-5,5])\n", "f_ax8.axis([0,1.1,-5,5])\n", "f_ax9.axis([0,1.1,-5,5])\n", "f_ax10.axis([0,1.1,-5,5])\n", "f_ax11.axis([0,1.1,-5,5])\n", "\n", "# the wing is in the 0th loop for the 6 9 10 \n", "\n", "f_plot_cp_profile_1(f_ax,get_case_root(case_name,num_procs),f_nx_1,f_ny_1,f_nz_1,f_x_loc_1,f_y_loc_1,f_z_loc_1) #ok\n", "\n", "\n", "f_plot_cp_profile_2(f_ax2,get_case_root(case_name,num_procs),f_nx_2,f_ny_2,f_nz_2,f_x_loc_2,f_y_loc_2,f_z_loc_2) # ok\n", "f_plot_cp_profile_4(f_ax4,get_case_root(case_name,num_procs),f_nx_4,f_ny_4,f_nz_4,f_x_loc_4,f_y_loc_4,f_z_loc_4) # ok\n", "f_plot_cp_profile_5(f_ax5,get_case_root(case_name,num_procs),f_nx_5,f_ny_5,f_nz_5,f_x_loc_5,f_y_loc_5,f_z_loc_5) # ok\n", "f_plot_cp_profile_6(f_ax6,get_case_root(case_name,num_procs),f_nx_6,f_ny_6,f_nz_6,f_x_loc_6,f_y_loc_6,f_z_loc_6) # ok\n", "f_plot_cp_profile_7(f_ax7,get_case_root(case_name,num_procs),f_nx_7,f_ny_7,f_nz_7,f_x_loc_7,f_y_loc_7,f_z_loc_7) # ok\n", "f_plot_cp_profile_8(f_ax8,get_case_root(case_name,num_procs),f_nx_8,f_ny_8,f_nz_8,f_x_loc_8,f_y_loc_8,f_z_loc_8) # ok\n", "f_plot_cp_profile_9(f_ax9,get_case_root(case_name,num_procs),f_nx_9,f_ny_9,f_nz_9,f_x_loc_9,f_y_loc_9,f_z_loc_9) # ok\n", "f_plot_cp_profile_10(f_ax10,get_case_root(case_name,num_procs),f_nx_10,f_ny_10,f_nz_10,f_x_loc_10,f_y_loc_10,f_z_loc_10) # ok\n", "f_plot_cp_profile_11(f_ax11,get_case_root(case_name,num_procs),f_nx_11,f_ny_11,f_nz_11,f_x_loc_11,f_y_loc_11,f_z_loc_11) # not\n", "f_ax.plot(f_loop_data_1[0][0],f_loop_data_1[0][1], color='r', markersize=20)\n", "f_ax2.plot(f_loop_data_2[0][0],f_loop_data_2[0][1], color='r', markersize=20)\n", "f_ax4.plot(f_loop_data_4[0][0],f_loop_data_4[0][1], color='r', markersize=20)\n", "f_ax5.plot(f_loop_data_5[0][0],f_loop_data_5[0][1], color='r', markersize=20)\n", "f_ax6.plot(f_loop_data_6[0][0],f_loop_data_6[0][1], color='r', markersize=20)\n", "f_ax7.plot(f_loop_data_7[0][0],f_loop_data_7[0][1], color='r', markersize=20)\n", "f_ax8.plot(f_loop_data_8[0][0],f_loop_data_8[0][1], color='r', markersize=20)\n", "f_ax9.plot(f_loop_data_9[0][0],f_loop_data_9[0][1], color='r', markersize=20)\n", "f_ax10.plot(f_loop_data_10[0][0],f_loop_data_10[0][1], color='r', markersize=20)\n", "f_ax11.plot(f_loop_data_11[0][0],f_loop_data_11[0][1], color='r', markersize=20)\n", "\n", "# SELECT THE LOOPS OF INTEREST AND NORMALIZE THEM \n", "# select the rigth loop for the flap\n", "\n", "#ps1\n", "f_min_loop_1 = min(f_loop_data_1[0][0]); f_max_loop_1 = max(f_loop_data_1[0][0]);\n", "f_norm_loop_1 = np.zeros(len(f_loop_data_1[0][0])) ;\n", "f_d1 = f_max_loop_1-f_min_loop_1 ;\n", "for i in range (0,len(f_loop_data_1[0][0])):\n", " f_norm_loop_1[i] = (f_loop_data_1[0][0][i]-f_min_loop_1)/f_d1\n", "#ps2\n", "f_min_loop_2 = min(f_loop_data_2[0][0]); f_max_loop_2 = max(f_loop_data_2[0][0]);\n", "f_norm_loop_2 = np.zeros(len(f_loop_data_2[0][0])) ;\n", "f_d2 = f_max_loop_2-f_min_loop_2 ;\n", "for i in range (0,len(f_loop_data_2[0][0])):\n", " f_norm_loop_2[i] = (f_loop_data_2[0][0][i]-f_min_loop_2)/f_d2\n", "#ps4\n", "f_min_loop_4 = min(f_loop_data_4[0][0]); f_max_loop_4 = max(f_loop_data_4[0][0]);\n", "f_norm_loop_4 = np.zeros(len(f_loop_data_4[0][0])) ;\n", "f_d4 = f_max_loop_4-f_min_loop_4 ;\n", "for i in range (0,len(f_loop_data_4[0][0])):\n", " f_norm_loop_4[i] = (f_loop_data_4[0][0][i]-f_min_loop_4)/f_d4\n", "#ps5\n", "f_min_loop_5 = min(f_loop_data_5[0][0]); f_max_loop_5 = max(f_loop_data_5[0][0]);\n", "f_norm_loop_5 = np.zeros(len(f_loop_data_5[0][0])) ;\n", "f_d5 = f_max_loop_5-f_min_loop_5 ;\n", "for i in range (0,len(f_loop_data_5[0][0])):\n", " f_norm_loop_5[i] = (f_loop_data_5[0][0][i]-f_min_loop_5)/f_d5\n", "#ps6\n", "f_min_loop_6 = min(f_loop_data_6[0][0]); f_max_loop_6 = max(f_loop_data_6[0][0]);\n", "f_norm_loop_6 = np.zeros(len(f_loop_data_6[0][0])) ;\n", "f_d6 = f_max_loop_6-f_min_loop_6 ;\n", "for i in range (0,len(f_loop_data_6[0][0])):\n", " f_norm_loop_6[i] = (f_loop_data_6[0][0][i]-f_min_loop_6)/f_d6\n", "#ps7\n", "f_min_loop_7 = min(f_loop_data_7[0][0]); f_max_loop_7 = max(f_loop_data_7[0][0]);\n", "f_norm_loop_7 = np.zeros(len(f_loop_data_7[0][0])) ;\n", "f_d7 = f_max_loop_7-f_min_loop_7 ;\n", "for i in range (0,len(f_loop_data_7[0][0])):\n", " f_norm_loop_7[i] = (f_loop_data_7[0][0][i]-f_min_loop_7)/f_d7\n", "#ps8\n", "f_min_loop_8 = min(f_loop_data_8[0][0]); f_max_loop_8 = max(f_loop_data_8[0][0]);\n", "f_norm_loop_8 = np.zeros(len(f_loop_data_8[0][0])) ;\n", "f_d8 = f_max_loop_8-f_min_loop_8 ;\n", "for i in range (0,len(f_loop_data_8[0][0])):\n", " f_norm_loop_8[i] = (f_loop_data_8[0][0][i]-f_min_loop_8)/f_d8\n", "#ps9\n", "f_min_loop_9 = min(f_loop_data_9[0][0]); f_max_loop_9 = max(f_loop_data_9[0][0]);\n", "f_norm_loop_9 = np.zeros(len(f_loop_data_9[0][0])) ;\n", "f_d9 = f_max_loop_1-f_min_loop_9 ;\n", "for i in range (0,len(f_loop_data_9[0][0])):\n", " f_norm_loop_9[i] = (f_loop_data_9[0][0][i]-f_min_loop_9)/f_d9\n", "\n", "#ps10\n", "f_min_loop_10 = min(f_loop_data_10[0][0]); f_max_loop_10 = max(f_loop_data_10[0][0]);\n", "f_norm_loop_10 = np.zeros(len(f_loop_data_10[0][0])) ;\n", "f_d10 = f_max_loop_10-f_min_loop_10 ;\n", "for i in range (0,len(f_loop_data_10[0][0])):\n", " f_norm_loop_10[i] = (f_loop_data_10[0][0][i]-f_min_loop_10)/f_d10\n", "#ps11\n", "f_min_loop_11 = min(f_loop_data_11[0][0]); f_max_loop_11 = max(f_loop_data_11[0][0]);\n", "f_norm_loop_11 = np.zeros(len(f_loop_data_11[0][0])) ;\n", "f_d11 = f_max_loop_11-f_min_loop_11 ;\n", "for i in range (0,len(f_loop_data_11[0][0])):\n", " f_norm_loop_11[i] = (f_loop_data_11[0][0][i]-f_min_loop_11)/f_d11\n", "\n", " " ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, 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P9i7zX8eeN2uWdz378datlpWba1mFhebeyT5jTk7j/czevc3zc3Isa/p0s911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\nDgAAQHvIH5igvVnjlPDBpoD7UxFx9qx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+MYFXMPl6fHtP9NpaJpjAyJ/qqUDW197lEDrBVHe09//VUcGJMYGFIZGwrD8V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\nVPYMGmSUkyOlpnZ+Vy7nJci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vbid0sPCE6pLTGJAkQrW70bjammv4s369OZSjp5YMvegMeTZvbhjFytsoB5mZ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\ndJkDB0rFxVJMjLR+vTlfqNY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odAy+n5GtOk89ccKYlJRoFhMdVGvPqc747j91ypgtW4xZutT8dsAvzB80w6zR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\neaBqaqTUVGnLlsYmWgDCFyj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DqT0RbBnA33LB7Ke98fxs9eoV6aNBKHmfbyH7bvrDH4z56U9DdZhAQM25iegd\nyrtUby4Y/Y256ZS3zS/1oHlZl5g9CVnGpKaajxK+a37X+W5zacrbpmR9ZZu+JoRPxMOeUBaU/FVd\n9g50PAt0zbnT4m6T7a7OKzW0UZaMSUz0HSqFg2fA49lO3rsGT3p66Gq7eH+hDBtm1R7q0SOyNWpo\nwgVEh1DUZgrlj8dQfzf4ag4Riu23xfeYr32EY9/heu+bG8oEEo5ANZzrIjzcNaG7dKGGsNN4n28h\n+2765S+Nuf/+kBwjEGq+Qvkmn/+9e82lCW+Z/9GvzFKdZaqUaNalTDSz4h8wpyduiPhvPLRcxMOe\nUBaU/FVd9gxvWlKgc/Ns4+3ZRtn9OFBHdaHmGXDZdRDWFoVJ7w6dI4WCMwA7of5usGsOEartt8X3\nWKDaHqHcdzje+9ZcwwF/gumQFc4Qsu+mq682Zu7cUBwS0GbsPv+efbZ+5/Rac64Wmcf1U7Nd/czn\nGml+4nrSnP2tavr8aWf8hT1RN/R6IMEOAx1OnsP8/eEPoRv6224ozMLCxkNSv/JKZIYaD2ZoQgAA\nACCaDR8ulZZK8fHSqlVBlGmnTJFuvVU677w2OT4gXEpKpDPPlD74QLrxRuv3ZWysdLz+uCZpqX6u\nx3W23tNyfUvLYiZrf875+u3CMUrrFhPpQ4cf/vKWdhf2OFlurrR0aeNp+flWsBOJUMuT55cDQQ8A\nAADao7Q0qaLCepyZKW3fHmCFUaOsu6+jR4f92IC24q5Acffd0vjx0pEjVkWDmMoyfUfLdLbe04V6\nU0u65Olfk59QYWHkfofCP395S6tjukWLFmn48OEaOnSoHnroodZuLiq5XI3/5eRYJ0hLFRRYwU5e\nXuPtJCRY/6ekWP/n5DQEPC+9FNkTLCvLuhgS9AAAAKC9io+3/o+Nlfr1a1oeb2L3bqlPnzY5NqCt\nuH9fjh5t1XTLz5fWrZO6ZKTrdU3T/9PjurLLvzT+8FItXGj9fkX706qaPfX19Ro2bJjeffdd9evX\nT6effrqef/55jRgxomEHDqjZ43LZT4+Lk5KSrFCmb1+r9ktCgtXk6Z13rIR03LjG8woLpenTG2rw\n5OdbJ5rUkLA+/HBkmmo1h7vJmfs1hfs423p/AAAAcB53bfV+/aRPPrGmeZbHG3FXdzh0SIqhKQuc\nz7O7kiOVR/Sv99N0zmlleuu9Lvz+ilJha8b18ccf695779WiRYskSb/97W8lSXfccUdQO28vfIU9\nnnr0sPp7OHvlAAAgAElEQVS08X7s/Tw/X6qubuiDZ/Hi9hlceDY5GzRIGjAgvEGM5/58XpABAACA\nIOTlBS6P3zHjG9386kRd893t3GxEh1NeLlUMHK1uC+YqOXdcpA8HPoStGdfOnTvVv3//E88zMzO1\nc+fO1mwyKnkGC3ahfrdu0rFj1uPsbOuf29ixDc/dzbIKC63Aor0GPVJDkzN3raalSxXWKn6e+3N3\nzg0AAAC0hLs8fsopVq17u+Zc32ypU9WRTjRjQYeUliZlfS9byVvXRvpQ0EJxrVnZFUyVF0mzZ88+\n8Tg3N1e5ubmt2W2by8+3Bh2XrKqfw4ZZtTpTU6WJE61O3j780Jo/cKA0d25D9be5c63p3h0st/ea\nKYWFDa9pxgxrWjiDGM/9tdeADAAAANHB3WeJZ+3xgoLGZfROXWMVp2PcbETHNWaM9NlnkT4KeCgq\nKlJRUVFQy7aqGdfy5cs1e/bsE824HnzwQcXExOiXv/xlww5cLk2aZBzV14rn8O+3324Nh15WZtXg\nWbLEGa+xOTzfj4722gEAANB++WvOVbFhh46d/i3F7tpBGRcd02uvSdOn69qcz7Wn5yjH/J53krD1\n2XPs2DENGzZM7733nvr27avx48fbdtAsWbtwYl8rnncDpk+X5s+P6OEAAAAACJLfm5alpdbd3NLS\niBwbEHaVlbrnuh2q+XKHMlylGhC7S0c2bVPG4WL1Mbs0SuslSS8pXz/QS478Pd/e+Qt7WtWMKy4u\nTk888YTOO+881dfX67rrrmsU9HhyavVHz75k3E22AAAAAEQ/d3MuW7GxDR1zAu2JMVJlpWbdsFvV\nX+5Uf/ONfnzhdq16dbs6792u3nXb1b1mu2SO64fK1E710y711d6ufbThULaKNV271FfbNEiVStVl\nelm/H3dcc+YwKl170qqaPUHtwOVSfr5xbBMfmjABAAAADlRWJg0ebP0PRJox0oEDeuD6raraslc9\nXAf0kx8cUNfaA9KBA/r0nf3qdKBUPep2K0O75YqJ0Y7jffTVoUxtV38ljhig4mP99d5X/bVd/bU7\ntr8O1KdKsvrhzcmxfs+++27j3RYlX6hJVW+pquhTJU86re1fN/wKW82eYDm5qpffuwEAAAAA2o2C\nAmnzZqv2fuGfY5VGzR60pePHdecV3+jYhi+VVb9V1393q7rs3CJt3Wr9i4vTJXWD9HV1hg6ou94/\n3F0XXN1dGjNGbyzrrvcrM7RbfXTG9Aw9Mz9ZBV59Us2YIS38ynputko6aH3Wc3Ol556zDuHaa6Wj\nR63H8fHShmXZmqS39Gjeu7pm42nKyorUm4PmapOaPWHeBQAAAAC0mmd/nFd8v1b/XNRDqq2N6DHB\ngerqpC1bpE2bpE2b9NHfN6lb6QZl1X6hqtg0rTs6XFs0REmnDtYVvx5s1TAbPFhKT/fZqbjddO9W\nKJ7PKyqkM8+UPvhAfgOcMSnbtLZqsL7WEE3O/Frbt7fNW4TghK2D5tbuHAAAAACiRaMfzG8eUVr/\n5IZqDkBzHD+u26/arS8XbdOAw5t1Zs8vdfEpmxS/ZZNUUiL17y8NHy4NG6aH3xim+ZtHaqNOUdeM\nNJWW2o8QJ/nuRiRc3Yv07CndvP9O3anf6ptNtRowrGvoNo5WI+wBAAAAgADKy6XTTpP69pXSkuv1\nxjud5Kqvj/RhIUrdeH2dKj8v0eDjX+vOy7YoYfcWq8bOFqvp1cFjKfqybpC+0lBt0nB1O2O4bn16\nmHTSSVLnzie24xkyvvKKdNtt0dMnbEmJNHniUW3d2VnXjftMu3uNYQj2KELYAwAAAABBaGjKZWQU\nI9XXSzGMQtTR/PePDql8w071c+3SPTfsUmLFLmnXLn2yYJcS9n2jjKMlSjuyRztMP23REJnBJ2nK\nj4dIQ/7v3+DByvtBshYutLaXnS0tWWIfkrSHQX9yJxktXWZ15swQ7NEj4h005+WJ9A8AAABA1EtI\nsP7PyXHJrIm1avYQ9jhTZaVVC+frr61/Ho8f27Vf2401JPnanX317Uv7Sn37anH8WL1T2V/faIDq\ne/fTjj3xJ5pcyev3bmGh1eGxMdK8eb5/D7eHQX8SEhtG7ZozJ8IHg6C0Sc0eyZD+AQAAAIh6jWpZ\nZHSxJnTpEunDQkv93whXx9dv1EnHNunqCZvUeduXqly5SfFHqrU78SRlThqiTiNOsppXDRkinXSS\nLvxxpt5aFOu3I+Roa3IVTu2h9lFHFPFmXDk5xrZzKQAAAACIWomJ0p49UlJSpI8EwTh+XPryS+mT\nT1T0u0/Ua8dqDazdqOrYVK09OkJfaIQSxg7X9b8frkvvGqZ/Le8ryWVbMaGtO0IGWiLiYU9ZmeFE\nAAAAANC+pKZK33xj/Y+oc8tVexX/2UqNPvSJ8gcsV/yalVK3btKECfrTigl6ccs4rdco2xGufA1h\nDrQnEQ976KAZAAAAQLvTvbu0ebP1PyLryBFpzRrpo4/06RMfKXPXCnU+WqWVJkefaILiJn5Ld7w6\nXurVS1Lg5lbU0IETEPYAAAAAQHP16qVbzv1cn+7orYQEBp1pU8eOSStXSu++qy+fek8D9qzSrsSh\nyrzs23r4w29r3qYJ2qIhkly2tXMIc9ARRHw0LgAAAABod+LiVLylXkuXW08LChh0Jmyqq/XwD1cr\necNyDd29VOPrPtTehIHqd/XZ+lv3X+qp3WequipZ+ZVS9SBpyyZp7FhpwAD7ka7awwhXQDgR9gAA\nAACAndhYJXU5Jokhp0PKGM36wZc69PYyZR9erm/Hr9RAs1UXxJ6qd6vG60n9SJdrnvZX9VT+Xqm6\nv1S9vvHfgFo7gH9t0oxr6lRDlUcAAAAA7cugQapc8L6uv38QwUJr7d6tf1y+UH03vafTKpaoTvF6\n+0iuPtYZWqnTdfIlp6qitpMWLrT6w66oaOg8WSLcAexEvM8eydgOZwcAAAAAUeukk6xefocOjfSR\ntD/G6L5L1yrz09c1sfxNDdVXKup0rgr3TdH7+q4O9R6k0j0uSVJ2trRkibVaQYH08MNNO1QG0FTE\nw56cHMNwdgAAAADal+HDpQULrP8RmDHSZ59JL72kvU++rJpqo1ePT9ebulAZl5ypitr4RiNk3XST\ntYpdnzsAAot42FNWZjh5AQAAALQvo0ZJL7xg/Q/fdu6UnnlGpQ/NU/2RY/qw72ValJyvuWvHSnIp\nPV3autValOZYQOhEfDQuTmQAAAAA7U5srDUEOJo6elR/yXtN2av/rlOqP1GnGZfqvkHz9NRn35K2\nupSRYS2Wni6tWdPwm5CuPYC2ERPpAwAAAACAqBQXJ9XXR/ooosuWLdIdd0gDBmjCqv/Vn8quUO+6\nHbqqdo6K+5whyaWcHGn5cik/36rRk5UV6YMGOh6GXkdIFBRImzdLCQli5DUAAAA4AzV7LMeP6/EL\n3tbo9/+gUXWrVdT/ap37xlL96p5hJ/rgsRsSnVo8QOS0Sc2e3FwpL08qL2+LvSESNm+Wli61Biso\nKAjddgsK+PwAAAAgQjp6zZ5Dh6Snn5ZGjVLef+7U3KMzlGm2K/+bR3T9w8NUWGjV3nEPxuMOeLjx\nC0Remw29Lonh1x0sL08nUv1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PkiVLAgp7BgyYZ4YMmWfuuGOeWblyZav9hHpnzRtPAYWvu1ozZ7a86xZuWBHp\n9xNsOd7uIgZzsubkegLkfqB7u5vkfmcp0DuQ7fVzA4DOLNyLrkgG4ZHYVzCBUaRaWoVSpms4Ee7+\n/V2k+9ufrwv/YFpleFru7wLcNRzxFhZ4Ck1cX3Pd3lMZgXIPetLTW+63PQQbnPj6+QYbtrj/vP2V\n1VGFdJxPnmzMP/7RpvUCYo3rMT8mc4vZplyTkkLLnlizcuVKM2/evONfbRb2/Otf/2rRjev+++83\nDz74YMsCJL9pejjNuD0FG54CikCbvbo/HziwOYAYOrQ5RLryytYBhjMkcm0GHG4Td18CCW983W1z\n8vVP0Pmz8HSgB3o3yt8dSE/Np9vy5wYAsI9gLkgj1dIq2DLdw4lw9+/vIt3f/nxd+AfTKqOjhwGu\n5ycJCVzQ2JX732lANxenTDHm9dfbq4pAzCnb3GDq1NVs/ao22lWBH20W9tTX15tBgwaZLVu2mKNH\nj5rRo0ebjRs3tipcMiYtzXtf9UiNPeAMCTwFFIE0e/XUpNi1L3V8fPPjxMTWAYZ7aBRqs3Rv3aOC\nvdPmfhcxlJM1b32p3bfzdWfJ1x1IT82nGSMHAAC0Nef5SWYmQU9n4n7D12PoM22aMUuXRqN6QOwY\nPtyYzz+Pdi3gR5uFPcYY88Ybb5hhw4aZwYMHm/vvv99j4Tk5vltshHNnyFOw4Smg8Nfs1VuTYtdA\nxdPgfJ76k+flhdcMONDB9nyFN8HeRWxLvu5Aur7XaDSfBgAAnVNHb5mE0DjP7dPSfFyfXHqpMYsX\nR6V+QMy46CJjXn452rWAH77CnrCmXg90KrApU0zIU9y68jQFZGWl9KMfSQ6HtHBh5KeIc52urqpK\nOvNM6b33pPR0a/nDD0tz5rSezi6Yeri/rxkzmqebzMiQVqwI/2cXq5xTa2ZmSuvXN0+5CQAA0JaG\nD5fKy6WEBGntWs5BOgvnuX1FhY9z7CuvlAoLre9AZ3XbbVKfPtLPfhbtmsAHX1Ovt0vYU1FhggpB\nPIU6klRQIK1aZT0uKpIWLWqzakdUcbH02mvS0aPS2LHWMbN1a/P7Kyxs+b4WLGgOjZzbBxsgdRSu\nYZod3x8AAIhNGRnWjTxJys2Vtm+Pbn3Qvnyeg159tXT++dI110SlbkBMeOIJacMG6X/+J9o1gQ++\nwp749qhARob1VVjYOsDxZNOm5vCjuLg51ElJsb7n5zcHIdFUXCz97/9aDUAlKSnJ+jp6VMrLk7Ky\nrPe6aZMyapnQAAAgAElEQVR150iy7iBkZ0v79zfvw/19ZWS0DLI6SqgVCvf3CgAA0B4SEqzvKSlW\nq210Lp7OQZ03nH/+bYImnNGglOhUDYgNQ4ZIf/tbtGuBMMS1V0HOAGfZMuuD1Bdn+JGWZjWxrKy0\nnpeUWC1fYqU706ZNzUGPZIU81dXW948+st7r0KHSF180rzNmjBUESc3hTqy9LwAAALubNElKTJTG\njbO65wPO65WynfF6dmFDtKsDRNeQIdI330S7FghDu4U9wbTKKSmxWr/U1FgtYZzhkDOBj5VAJMVP\n3N+li9WCZ/9+q+tWYaH0zjvS4sUtw51Ye18AAAB2t3u3dOyYdXHv70YkOgfnuX1mToKuubI+upUB\noq1/f2nPHunIkWjXBCFqt7AnmNYrGRnWXRYpdrpseVJSYo0j5HC0viOUmWkN5ixJcXFWa5/f/55w\nBwAAIBbE2vAAiD7n9crFl8QrJZGWPeikGht128z9uvaMr6SjRzUz63Xl5FhjzqJjaZcBmt2L8DYA\ns6uONnCvc1apMWOsEPTpp63lOTlSw//9r2DwPwAAgNjQ0c410Y5uu03q21e6/fZo1wR2d/Sobr+h\nSrs21SgzoUYP3X1Y3eIOW11cDrt8P3xYby6pUc2Bo1Jigi4qTFDX7onW4GOJ1veSxQnat7dJXeMb\nNfOHjUpOaNDLSxp1cH+jkuKblJbSpK2bG5TYUKtBWZXKjq9Ql+pKxdUeUkaXGsXX16qbapTSdEg1\nXdK1s6GnTtaXkiSHDNeyMSrqAzS78zYAs6vTT7cGNR46tHk6TNeQyJkubt5svda9u/+Bn9tSSYnn\nE4aMDKsbF4P/dQyBBJEAAKDjY5IIeJWQ0Hy3FjHvxhsatfPLah38rlIDeh9TemqDfn1/g7qnNEgN\nDXrovgaVbz2qbolHdcctR/Xcn49pz+5GdU00+slPpNQUIxmjp56yrj8TE6Uf/8ShF150aFe5Q10T\nmvSTHxulJjdZA7Y2ef9e8rzRwT3HlNbliDK61qnhQLXSVaWJ36tWYm2VNQWg61djo+5Wug42dFON\n0rT7qlR1y0u1Bq9Nbfl926FMfbkzSfFqUP3yY7rqsnorCKqslI4dU0pZvbrsitMRddGKZ+N18fQu\nOrwnXru3x6lRXZScEqdDtV1Vqyyt3T1STd0ztbU6Q1VKV019mmqVosNKVaUy1DM7TuXl0v86btD1\n5il1S27Qe+9FJTpAGKLyG3NvNuvpAru8vHk6zDPPtFJE15AoJ0fat896vGOH9d01OHLu0z0Mmju3\ndVnuU6MvXhz8Rb77CYOz/FNOsca1+uADqx6IbYEEkQAAALCx+HipvnnMHm4Gto/iYmn7lzU68s12\njcwuV9/4PfrplXuUUr1HS57Yo7TDe5WuKo0ddkiJRw9Jh6yvJ+qOqkZpqlSGjpYnqUHxOnhxgroP\njpfi43XhV120rzpJR5WkjXOTdNKxRHXfb10Gr7/XYQ294XCo/0aHkvZarVjW/8po4BGjtH1GTYrT\nuvvjdNbZDmt8Dof37yllDu3dlahyddW+lK7aXdtfVUrXhv7puv2edGvsj/R06+I0PV1KTtZVFzq0\nbJl1bbx8uSQvf19LP5aWbbHW+7mH9RZ8Iy3b1XI/JZ9Ly7ZbyzIyrPFwJWvCoOxsaeUKqxpVVc3f\n8/OlJUukOXOkCwbnSQ9KXy39Sn0GjGyj3zzajGljnoqoqDCmqMj6bowxEycaK06VtdwYY7Kzrecp\nKcaUlVnLpkyxluXnGzNpkvU4Pb15mXN/7vt03benstzXdS6fPdt6LTfXmAkTrPIrKpqXO5974qkc\nxD7XvzFvv1sAAADY2Lx51tf/4bw+Qhobjdm505j33zempMT8ddwD5uU+N5mPci40DaeMMtXxGeaw\nks3XGmpKdbZ5SUXmjSE3G3PvveY/kheYaVpqJmqlmXLCWmO+/tqYXbuMOXTITL2g0ed1ofv5vbfz\n/UDX88XT9aq/7d2vjUNdz9PrrssqKowpLDRm+vTm50VF1rW26/cW+1+2zHoTzz4b2A8A7c5XpBOV\nsMedpwOprMwKWZxBjzGt/1i9/lG67NP9oPdUlnOZZExenucQyl9g5OQMgpKSmst3fQ/OdZKTjYmP\nNyYrq/XriJ5AP2wBAABgU/fea8xddx1/ys3AwBVf32CuPG2z+UX+MnP4oT8Y8x//YValXmC+jRti\n6pRkGrJ7GnPaacYUFZkXcn9mfqLHzMV6xcyZtM5ccd5+IzV5DG08NQRw8ndd6H5+7+18P9D1fPF0\nvdqh/2a++cb6wd92W7RrAi985S1RGaDZXVsMkOfc58MPW03QnPv2VFZlpXTttVZ88/TTzcudgy67\nNmlbvlyaMUMtmtq51rmgoLkbkFNRUcvuQO7r+BrsytM4RTQhBQAAANrIQw9JBw9a38Vg3p4Uzzaq\n+mybhjd8rjsv+lzJmz+XPv9cRzZ8rb1N2dqkYWoaNFT/dvMw/eAXQ/Vp7RBt1QBl5yYfv+5xXmsd\n73Ykz9dvknUNdOaZ1hioDI3RjhoarDGsJkxgANoY5StviYmwJ1YFExg5eQuIXNdzriNJycnSl196\n/9ByDYZcxylyD5Ak7+MU8U8JAAAACNBvfyvt2mV9h2XvXmnNGr1292rllK3RkIo1qjfx2qBROjZs\npC6+c6R0yim69Bcn6+XlaS2ugXJymies2bix+bqHEK2D6N7dGh+pqckanwgxhbCnHXkLiNzX+eEP\npU8+8T9ws2vi7RxUy1OAJAXWqkgiFAIAAAC8evRR60T5D3+Idk2iZ+dOqbRUWrlSexeXKrn2gL5N\nz9dnyeP08o5xWq3TtEt9W12XeApwaJXTsX3eb4pG7nhTPyoo0+9fHsA1Y4wh7OnAXD8wJd/pdyCt\niiTfoZC32QaYhQAAAACdwhNPSJ9/bn3vLA4f1hOXLFfu58s0tmqlenc9qLiCidI55+javxToL2tP\nllGcevWyZk0eM0bq37/lEBiwp6cG/krXl/2XCvWyEosKma04xvjKW+LauS4IknNK94yMlo89KSmx\nQptPP7W+ewp6JCuwkaxQSLJCIWeY5Jx6fNkyK+Bx8ra8uNgKj6ZObQ6mXJ97WgcAAACIWW5Tr9tR\ncbFUdMYOPX7Kk6qfPFXq3Vvj1zymlbuHa0rtEl117l7pr3+Vbr5Ze3JGyihO+fnShx9a1xnvvCMt\nXUrQ0xn8q/8PJEkX9157/JoRHQMtezohX13N3AdK87fctZVQUZHVndf1+aJFrdfJyJBee006elQa\nO1bq04eBpwEAABAjFi6U3n3X+m43O3ZIL72kT+98QbkNW7RMU7Q7/2LNWXGBpl6V7vF8n7F1OrfK\nSikj0yGTlibHoUPRrg7c+Mpb4tu5LogBzhZCUuvxfEpKPH+Ye1vubCXkbB00Y0bL557WKSy0mn9K\n1hhErgNPFxe3rhMAAIDduXeZnzvX+xiLvrrX++qS73qzbfFi/931fY3zGEjXf9eZZH3NKhvMe/f3\nPocPt84zExKktWubx4gJakgCu7XsOXhQWrLEeuMbNkiXXKJ5XR/S6zUT1ah4FeZKSvd+vu967YDO\nJyND0o03yvHHP0a7KghWG03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"text": [ "" ] } ], "prompt_number": 12 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "## COMPARE THE DATA WITH THE EXPERIMENTAL RESULTS\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from matplotlib.backends.backend_pdf import PdfPages\n", "from IPython.display import FileLink, display \n", "\n", "from collections import OrderedDict\n", "import matplotlib.font_manager as fm\n", "prop=fm.FontProperties(size=20)\n", "\n", "## VALIDATION OF THE WING \n", "## we need to plot the whole section from 0 to 1 and not separately from 0 to 1.....Applied only to experimental \n", "## we need to define eta = x_local / semispan\n", "\n", "#eta = ['0.15' , '0.288' , '0.449' , '0.543' , '0.681' , '0.715' ,'0.818' , '0.891' ,'0.964']\n", "\n", "comp_fig1 = pl.figure(figsize=(40, 10),dpi=150, facecolor='w', edgecolor='k')\n", "comp_fig1.suptitle('PS1', fontsize=30, fontweight='bold')\n", "\n", "comp_ax =comp_fig1.add_subplot(1,3,1)\n", "comp_ax_w =comp_fig1.add_subplot(1,3,2)\n", "comp_ax_f =comp_fig1.add_subplot(1,3,3)\n", "\n", "comp_ax.grid(True)\n", "comp_ax.set_xlabel('x/c', fontsize=20, fontweight='bold')\n", "comp_ax.set_ylabel('Cp []', fontsize=20, fontweight='bold')\n", "comp_ax.set_title('Slat', fontsize=20, fontweight='bold')\n", "comp_ax.axis([-0.001,1.01,1.5,-4.])\n", "\n", "comp_ax_w.grid(True)\n", "comp_ax_w.set_xlabel('x/c', fontsize=20, fontweight='bold')\n", "comp_ax_w.set_ylabel('Cp []', fontsize=20, fontweight='bold')\n", "comp_ax_w.set_title('Main Wing', fontsize=20, fontweight='bold')\n", "comp_ax_w.axis([-0.001,1.01,1.5,-4.])\n", "\n", "comp_ax_f.grid(True)\n", "comp_ax_f.set_xlabel('x/c', fontsize=20, fontweight='bold')\n", "comp_ax_f.set_ylabel('Cp []', fontsize=20, fontweight='bold')\n", "comp_ax_f.set_title('Flap', fontsize=20, fontweight='bold')\n", "comp_ax_f.axis([-0.001,1.01,1.5,-4.])\n", "\n", "comp_ax.plot(cp_ps01_slat[0],cp_ps01_slat[6],'b.', color='b', markersize=25,label = 'Experiment')\n", "comp_ax.plot(s_norm_loop_1,s_loop_data_1[2][1],'b.', color='r',markersize=20,label = 'zCFD')\n", "comp_ax_w.plot(cp_ps01[0],cp_ps01[6],'b.', color='b', markersize=25, label = 'Experiment')\n", "comp_ax_w.plot(w_norm_loop_1,w_loop_data_1[1][1] ,'b.',color='r',markersize=20,label = 'zCFD')\n", "comp_ax_f.plot(cp_ps01_flap[0],cp_ps01_flap[6],'b.', color='b', markersize=25,label = 'Experiment')\n", "comp_ax_f.plot(f_norm_loop_1,f_loop_data_1[0][1], 'b.', color ='r', markersize=20,label = 'zCFD')\n", "\n", "comp_ax.legend(loc = 'upper right',numpoints=1,prop = prop)\n", "comp_ax_w.legend(loc = 'upper right',numpoints=1,prop = prop)\n", "comp_ax_f.legend(loc = 'upper right',numpoints=1,prop = prop)\n", "\n", "pp1 = PdfPages('11.pdf')\n", "pp1.savefig()\n", "pp1.close()\n", "comp_fig1.savefig(\"11.png\")\n", "show()\n", "display(FileLink('11.png'))\n", "# station 2\n", "\n", "comp_fig2 = pl.figure(figsize=(40, 10),dpi=600, facecolor='w', edgecolor='k')\n", "comp_fig2.suptitle('PS2', fontsize=30, fontweight='bold')\n", "\n", "comp_ax_2 =comp_fig2.add_subplot(1,3,1)\n", "comp_ax_w_2 =comp_fig2.add_subplot(1,3,2)\n", "comp_ax_f_2 =comp_fig2.add_subplot(1,3,3)\n", "\n", "comp_ax_2.grid(True)\n", "comp_ax_2.set_xlabel('x/c', fontsize=20, fontweight='bold')\n", "comp_ax_2.set_ylabel('Cp []', fontsize=20, fontweight='bold')\n", "comp_ax_2.set_title('Slat', fontsize=20, fontweight='bold')\n", "comp_ax_2.axis([-0.001,1.01,1.5,-4.])\n", "\n", "comp_ax_w_2.grid(True)\n", "comp_ax_w_2.set_xlabel('x/c', fontsize=20, fontweight='bold')\n", "comp_ax_w_2.set_ylabel('Cp []', fontsize=20, fontweight='bold')\n", "comp_ax_w_2.set_title('Main Wing', fontsize=20, fontweight='bold')\n", "comp_ax_w_2.axis([-0.001,1.01,1.5,-4.])\n", "\n", "comp_ax_f_2.grid(True)\n", "comp_ax_f_2.set_xlabel('x/c', fontsize=20, fontweight='bold')\n", "comp_ax_f_2.set_ylabel('Cp []', fontsize=20, fontweight='bold')\n", "comp_ax_f_2.set_title('Flap', fontsize=20, fontweight='bold')\n", "comp_ax_f_2.axis([-0.001,1.01,1.5,-4.])\n", "\n", "comp_ax_2.plot(cp_ps02_slat[0],cp_ps02_slat[6],'b.', color='b', markersize=25,label = 'Experiment')\n", "comp_ax_2.plot(s_norm_loop_2,s_loop_data_2[2][1],'b.', color='r',markersize=20,label = 'zCFD')\n", "comp_ax_w_2.plot(cp_ps02[0],cp_ps02[6],'b.', color='b', markersize=25,label = 'Experiment')\n", "comp_ax_w_2.plot(w_norm_loop_2,w_loop_data_2[1][1] ,'b.',color='r',markersize=20, label = 'zCFD')\n", "comp_ax_f_2.plot(cp_ps02_flap[0],cp_ps02_flap[6],'b.', color='b', markersize=25, label = 'Experiment')\n", "comp_ax_f_2.plot(f_norm_loop_2,f_loop_data_2[0][1], 'b.', color ='r', markersize=20, label = 'zCFD')\n", "\n", "comp_ax_2.legend(loc = 'upper right',numpoints=1,prop = prop)\n", "comp_ax_w_2.legend(loc = 'upper right',numpoints=1,prop = prop)\n", "comp_ax_f_2.legend(loc = 'upper right',numpoints=1,prop = prop)\n", "\n", "pp2 = PdfPages('22.pdf')\n", "pp2.savefig()\n", "pp2.close()\n", "comp_fig2.savefig(\"22.png\")\n", "show()\n", "display(FileLink('22.png'))\n", " \n", "# station 4\n", "\n", "comp_fig4 = pl.figure(figsize=(40, 10),dpi=600, facecolor='w', edgecolor='k')\n", "comp_fig4.suptitle('PS4', fontsize=30, fontweight='bold')\n", "\n", "comp_ax_4 =comp_fig4.add_subplot(1,3,1)\n", "comp_ax_w_4 =comp_fig4.add_subplot(1,3,2)\n", "comp_ax_f_4 =comp_fig4.add_subplot(1,3,3)\n", "\n", "comp_ax_4.grid(True)\n", "comp_ax_4.set_xlabel('x/c', fontsize=20, fontweight='bold')\n", "comp_ax_4.set_ylabel('Cp []', fontsize=20, fontweight='bold')\n", "comp_ax_4.set_title('Slat', fontsize=20, fontweight='bold')\n", "comp_ax_4.axis([-0.001,1.01,1.5,-4.])\n", "\n", "comp_ax_w_4.grid(True)\n", "comp_ax_w_4.set_xlabel('x/c', fontsize=20, fontweight='bold')\n", "comp_ax_w_4.set_ylabel('Cp []', fontsize=20, fontweight='bold')\n", "comp_ax_w_4.set_title('Main Wing', fontsize=20, fontweight='bold')\n", "comp_ax_w_4.axis([-0.001,1.01,1.5,-4.])\n", "\n", "comp_ax_f_4.grid(True)\n", "comp_ax_f_4.set_xlabel('x/c', fontsize=20, fontweight='bold')\n", "comp_ax_f_4.set_ylabel('Cp []', fontsize=20, fontweight='bold')\n", "comp_ax_f_4.set_title('Flap', fontsize=20, fontweight='bold')\n", "comp_ax_f_4.axis([-0.001,1.01,1.5,-4.])\n", "\n", "comp_ax_4.plot(cp_ps04_slat[0],cp_ps04_slat[6],'b.', color='b', markersize=25, label = 'Experiment')\n", "comp_ax_4.plot(s_norm_loop_4,s_loop_data_4[2][1],'b.', color='r',markersize=20 , label = 'zCFD')\n", "comp_ax_w_4.plot(cp_ps04[0],cp_ps04[6],'b.', color='b', markersize=25 , label = 'Experiment')\n", "comp_ax_w_4.plot(w_norm_loop_4,w_loop_data_4[1][1] ,'b.',color='r',markersize=20, label = 'zCFD')\n", "comp_ax_f_4.plot(cp_ps04_flap[0],cp_ps04_flap[6],'b.', color='b', markersize=25 ,label = 'Experiment')\n", "comp_ax_f_4.plot(f_norm_loop_4,f_loop_data_4[0][1], 'b.', color ='r', markersize=20 ,label = 'zCFD')\n", "\n", "comp_ax_4.legend(loc = 'upper right',numpoints=1,prop = prop)\n", "comp_ax_w_4.legend(loc = 'upper right',numpoints=1,prop = prop)\n", "comp_ax_f_4.legend(loc = 'upper right',numpoints=1,prop = prop)\n", "\n", "pp4 = PdfPages('44.pdf')\n", "pp4.savefig()\n", "pp4.close()\n", "comp_fig4.savefig(\"44.png\")\n", "show()\n", "display(FileLink('44.png'))\n", "\n", "# station 5\n", "\n", "comp_fig5 = pl.figure(figsize=(40, 10),dpi=600, facecolor='w', edgecolor='k')\n", "comp_fig5.suptitle('PS5', fontsize=30, fontweight='bold')\n", "\n", "comp_ax_5 =comp_fig5.add_subplot(1,3,1)\n", "comp_ax_w_5 =comp_fig5.add_subplot(1,3,2)\n", "comp_ax_f_5 =comp_fig5.add_subplot(1,3,3)\n", "\n", "comp_ax_5.grid(True)\n", "comp_ax_5.set_xlabel('x/c', fontsize=20, fontweight='bold')\n", "comp_ax_5.set_ylabel('Cp []', fontsize=20, fontweight='bold')\n", "comp_ax_5.set_title('Slat', fontsize=20, fontweight='bold')\n", "comp_ax_5.axis([-0.001,1.01,1.5,-4.])\n", "\n", "comp_ax_w_5.grid(True)\n", "comp_ax_w_5.set_xlabel('x/c', fontsize=20, fontweight='bold')\n", "comp_ax_w_5.set_ylabel('Cp []', fontsize=20, fontweight='bold')\n", "comp_ax_w_5.set_title('Main Wing', fontsize=20, fontweight='bold')\n", "comp_ax_w_5.axis([-0.001,1.01,1.5,-4.])\n", "\n", "comp_ax_f_5.grid(True)\n", "comp_ax_f_5.set_xlabel('x/c', fontsize=20, fontweight='bold')\n", "comp_ax_f_5.set_ylabel('Cp []', fontsize=20, fontweight='bold')\n", "comp_ax_f_5.set_title('Flap', fontsize=20, fontweight='bold')\n", "comp_ax_f_5.axis([-0.001,1.01,1.5,-4.])\n", "\n", "comp_ax_5.plot(cp_ps05_slat[0],cp_ps05_slat[6],'b.', color='b', markersize=25, label = 'Experiment')\n", "comp_ax_5.plot(s_norm_loop_5,s_loop_data_5[2][1],'b.', color='r',markersize=20, label = 'zCFD') # the slat is not in the first loop\n", "comp_ax_w_5.plot(cp_ps05[0],cp_ps05[6],'b.', color='b', markersize=25, label = 'Experiment')\n", "comp_ax_w_5.plot(w_norm_loop_5,w_loop_data_5[1][1] ,'b.',color='r',markersize=20, label = 'zCFD')\n", "comp_ax_f_5.plot(cp_ps05_flap[0],cp_ps05_flap[6],'b.', color='b', markersize=25 , label = 'Experiment')\n", "comp_ax_f_5.plot(f_norm_loop_5,f_loop_data_5[0][1], 'b.', color ='r', markersize=20, label = 'zCFD')\n", "\n", "comp_ax_5.legend(loc = 'upper right',numpoints=1,prop = prop)\n", "comp_ax_w_5.legend(loc = 'upper right',numpoints=1,prop = prop)\n", "comp_ax_f_5.legend(loc = 'upper right',numpoints=1,prop = prop)\n", "\n", "pp5 = PdfPages('55.pdf')\n", "pp5.savefig()\n", "pp5.close()\n", "comp_fig5.savefig(\"55.png\")\n", "show()\n", "display(FileLink('55.png'))\n", "# station 6\n", "\n", "comp_fig6 = pl.figure(figsize=(40, 10),dpi=600, facecolor='w', edgecolor='k')\n", "comp_fig6.suptitle('PS6', fontsize=30, fontweight='bold')\n", "\n", "comp_ax_6 =comp_fig6.add_subplot(1,3,1)\n", "comp_ax_w_6 =comp_fig6.add_subplot(1,3,2)\n", "comp_ax_f_6 =comp_fig6.add_subplot(1,3,3)\n", "\n", "comp_ax_6.grid(True)\n", "comp_ax_6.set_xlabel('x/c', fontsize=20, fontweight='bold')\n", "comp_ax_6.set_ylabel('Cp []', fontsize=20, fontweight='bold')\n", "comp_ax_6.set_title('Slat', fontsize=20, fontweight='bold')\n", "comp_ax_6.axis([-0.001,1.01,1.5,-4.])\n", "\n", "comp_ax_w_6.grid(True)\n", "comp_ax_w_6.set_xlabel('x/c', fontsize=20, fontweight='bold')\n", "comp_ax_w_6.set_ylabel('Cp []', fontsize=20, fontweight='bold')\n", "comp_ax_w_6.set_title('Main Wing', fontsize=20, fontweight='bold')\n", "comp_ax_w_6.axis([-0.001,1.01,1.5,-4.])\n", "\n", "comp_ax_f_6.grid(True)\n", "comp_ax_f_6.set_xlabel('x/c', fontsize=20, fontweight='bold')\n", "comp_ax_f_6.set_ylabel('Cp []', fontsize=20, fontweight='bold')\n", "comp_ax_f_6.set_title('Flap', fontsize=20, fontweight='bold')\n", "comp_ax_f_6.axis([-0.001,1.01,1.5,-4.])\n", "\n", "comp_ax_6.plot(cp_ps06_slat[0],cp_ps06_slat[6],'b.', color='b', markersize=25,label = 'Experiment')\n", "comp_ax_6.plot(s_norm_loop_6,s_loop_data_6[2][1],'b.', color='r',markersize=20, label = 'zCFD') \n", "comp_ax_w_6.plot(cp_ps06[0],cp_ps06[6],'b.', color='b', markersize=25, label = 'Experiment')\n", "comp_ax_w_6.plot(w_norm_loop_6,w_loop_data_6[1][1] ,'b.',color='r',markersize=20, label = 'zCFD')\n", "comp_ax_f_6.plot(cp_ps06_flap[0],cp_ps06_flap[6],'b.', color='b', markersize=25, label = 'Experiment')\n", "comp_ax_f_6.plot(f_norm_loop_6,f_loop_data_6[0][1], 'b.', color ='r', markersize=20, label = 'zCFD')\n", "\n", "comp_ax_6.legend(loc = 'upper right',numpoints=1,prop = prop)\n", "comp_ax_w_6.legend(loc = 'upper right',numpoints=1,prop = prop)\n", "comp_ax_f_6.legend(loc = 'upper right',numpoints=1,prop = prop)\n", "\n", "pp6 = PdfPages('66.pdf')\n", "pp6.savefig()\n", "pp6.close()\n", "comp_fig6.savefig(\"66.png\")\n", "show()\n", "display(FileLink('66.png'))\n", "\n", "# station 7\n", "\n", "comp_fig7 = pl.figure(figsize=(40, 10),dpi=600, facecolor='w', edgecolor='k')\n", "comp_fig7.suptitle('PS7', fontsize=30, fontweight='bold')\n", "\n", "comp_ax_7 =comp_fig7.add_subplot(1,3,1)\n", "comp_ax_w_7 =comp_fig7.add_subplot(1,3,2)\n", "comp_ax_f_7 =comp_fig7.add_subplot(1,3,3)\n", "\n", "comp_ax_7.grid(True)\n", "comp_ax_7.set_xlabel('x/c', fontsize=20, fontweight='bold')\n", "comp_ax_7.set_ylabel('Cp []', fontsize=20, fontweight='bold')\n", "comp_ax_7.set_title('Slat', fontsize=20, fontweight='bold')\n", "comp_ax_7.axis([-0.001,1.01,1.5,-4.])\n", "\n", "comp_ax_w_7.grid(True)\n", "comp_ax_w_7.set_xlabel('x/c', fontsize=20, fontweight='bold')\n", "comp_ax_w_7.set_ylabel('Cp []', fontsize=20, fontweight='bold')\n", "comp_ax_w_7.set_title('Main Wing', fontsize=20, fontweight='bold')\n", "comp_ax_w_7.axis([-0.001,1.01,1.5,-4.])\n", "\n", "comp_ax_f_7.grid(True)\n", "comp_ax_f_7.set_xlabel('x/c', fontsize=20, fontweight='bold')\n", "comp_ax_f_7.set_ylabel('Cp []', fontsize=20, fontweight='bold')\n", "comp_ax_f_7.set_title('Flap', fontsize=20, fontweight='bold')\n", "comp_ax_f_7.axis([-0.001,1.01,1.5,-4.])\n", "\n", "comp_ax_7.plot(cp_ps06_slat[0],cp_ps06_slat[6],'b.', color='b', markersize=25, label = 'Experiment')\n", "comp_ax_7.plot(s_norm_loop_7,s_loop_data_7[2][1],'b.', color='r',markersize=20, label = 'zCFD') # \n", "comp_ax_w_7.plot(cp_ps07[0],cp_ps07[6],'b.', color='b', markersize=25 ,label = 'Experiment')\n", "comp_ax_w_7.plot(w_norm_loop_7,w_loop_data_7[1][1] ,'b.',color='r',markersize=20, label = 'zCFD')\n", "comp_ax_f_7.plot(cp_ps07_flap[0],cp_ps07_flap[6],'b.', color='b', markersize=25, label = 'Experiment')\n", "comp_ax_f_7.plot(f_norm_loop_7,f_loop_data_7[0][1], 'b.', color ='r', markersize=20, label = 'zCFD')\n", "\n", "comp_ax_7.legend(loc = 'upper right',numpoints=1,prop = prop)\n", "comp_ax_w_7.legend(loc = 'upper right',numpoints=1,prop = prop)\n", "comp_ax_f_7.legend(loc = 'upper right',numpoints=1,prop = prop)\n", "\n", "pp7 = PdfPages('77.pdf')\n", "pp7.savefig()\n", "pp7.close()\n", "comp_fig7.savefig(\"77.png\")\n", "show()\n", "display(FileLink('77.png'))\n", "\n", "# station 8\n", "\n", "comp_fig8 = pl.figure(figsize=(40, 10),dpi=600, facecolor='w', edgecolor='k')\n", "comp_fig8.suptitle('PS8', fontsize=30, fontweight='bold')\n", "\n", "comp_ax_8 =comp_fig8.add_subplot(1,3,1)\n", "comp_ax_w_8 =comp_fig8.add_subplot(1,3,2)\n", "comp_ax_f_8 =comp_fig8.add_subplot(1,3,3)\n", "\n", "comp_ax_8.grid(True)\n", "comp_ax_8.set_xlabel('x/c', fontsize=20, fontweight='bold')\n", "comp_ax_8.set_ylabel('Cp []', fontsize=20, fontweight='bold')\n", "comp_ax_8.set_title('Slat', fontsize=20, fontweight='bold')\n", "comp_ax_8.axis([-0.001,1.01,1.5,-4.])\n", "\n", "comp_ax_w_8.grid(True)\n", "comp_ax_w_8.set_xlabel('x/c', fontsize=20, fontweight='bold')\n", "comp_ax_w_8.set_ylabel('Cp []', fontsize=20, fontweight='bold')\n", "comp_ax_w_8.set_title('Main Wing', fontsize=20, fontweight='bold')\n", "comp_ax_w_8.axis([-0.001,1.01,1.5,-4.])\n", "\n", "comp_ax_f_8.grid(True)\n", "comp_ax_f_8.set_xlabel('x/c', fontsize=20, fontweight='bold')\n", "comp_ax_f_8.set_ylabel('Cp []', fontsize=20, fontweight='bold')\n", "comp_ax_f_8.set_title('Flap', fontsize=20, fontweight='bold')\n", "comp_ax_f_8.axis([-0.001,1.01,1.5,-4.])\n", "\n", "comp_ax_8.plot(cp_ps08_slat[0],cp_ps08_slat[6],'b.', color='b', markersize=25, label = 'Experiment')\n", "comp_ax_8.plot(s_norm_loop_8,s_loop_data_8[2][1],'b.', color='r',markersize=20, label = 'zCFD') # \n", "comp_ax_w_8.plot(cp_ps08[0],cp_ps08[6],'b.', color='b', markersize=25 ,label = 'Experiment')\n", "comp_ax_w_8.plot(w_norm_loop_8,w_loop_data_8[1][1] ,'b.',color='r',markersize=20, label = 'zCFD')\n", "comp_ax_f_8.plot(cp_ps08_flap[0],cp_ps08_flap[6],'b.', color='b', markersize=25, label = 'Experiment')\n", "comp_ax_f_8.plot(f_norm_loop_8,f_loop_data_8[0][1], 'b.', color ='r', markersize=20, label = 'zCFD')\n", "\n", "comp_ax_8.legend(loc = 'upper right',numpoints=1,prop = prop)\n", "comp_ax_w_8.legend(loc = 'upper right',numpoints=1,prop = prop)\n", "comp_ax_f_8.legend(loc = 'upper right',numpoints=1,prop = prop)\n", "\n", "pp8 = PdfPages('88.pdf')\n", "pp8.savefig()\n", "pp8.close()\n", "comp_fig8.savefig(\"88.png\")\n", "show()\n", "display(FileLink('88.png'))\n", "# station 9\n", "\n", "comp_fig9 = pl.figure(figsize=(40, 10),dpi=600, facecolor='w', edgecolor='k')\n", "comp_fig9.suptitle('PS9', fontsize=30, fontweight='bold')\n", "\n", "comp_ax_9 =comp_fig9.add_subplot(1,3,1)\n", "comp_ax_w_9 =comp_fig9.add_subplot(1,3,2)\n", "comp_ax_f_9 =comp_fig9.add_subplot(1,3,3)\n", "\n", "comp_ax_9.grid(True)\n", "comp_ax_9.set_xlabel('x/c', fontsize=20, fontweight='bold')\n", "comp_ax_9.set_ylabel('Cp []', fontsize=20, fontweight='bold')\n", "comp_ax_9.set_title('Slat', fontsize=20, fontweight='bold')\n", "comp_ax_9.axis([-0.001,1.01,1.5,-4.])\n", "\n", "comp_ax_w_9.grid(True)\n", "comp_ax_w_9.set_xlabel('x/c', fontsize=20, fontweight='bold')\n", "comp_ax_w_9.set_ylabel('Cp []', fontsize=20, fontweight='bold')\n", "comp_ax_w_9.set_title('Main Wing', fontsize=20, fontweight='bold')\n", "comp_ax_w_9.axis([-0.001,1.01,1.5,-4.])\n", "\n", "comp_ax_f_9.grid(True)\n", "comp_ax_f_9.set_xlabel('x/c', fontsize=20, fontweight='bold')\n", "comp_ax_f_9.set_ylabel('Cp []', fontsize=20, fontweight='bold')\n", "comp_ax_f_9.set_title('Flap', fontsize=20, fontweight='bold')\n", "comp_ax_f_9.axis([-0.001,1.01,1.5,-4.])\n", "\n", "comp_ax_9.plot(cp_ps09_slat[0],cp_ps09_slat[6],'b.', color='b', markersize=25, label = 'Experiment')\n", "comp_ax_9.plot(s_norm_loop_9,s_loop_data_9[2][1],'b.', color='r',markersize=20, label = 'zCFD') # \n", "comp_ax_w_9.plot(cp_ps09[0],cp_ps09[6],'b.', color='b', markersize=25, label = 'Experimet')\n", "comp_ax_w_9.plot(w_norm_loop_9,w_loop_data_9[1][1] ,'b.',color='r',markersize=20 , label = 'zCFD')\n", "comp_ax_f_9.plot(cp_ps09_flap[0],cp_ps09_flap[6],'b.', color='b', markersize=25, label = 'Experiment')\n", "comp_ax_f_9.plot(f_norm_loop_9,f_loop_data_9[0][1], 'b.', color ='r', markersize=20, label = 'zCFD')\n", "\n", "comp_ax_9.legend(loc = 'upper right',numpoints=1,prop = prop)\n", "comp_ax_w_9.legend(loc = 'upper right',numpoints=1,prop = prop)\n", "comp_ax_f_9.legend(loc = 'upper right',numpoints=1,prop = prop)\n", "\n", "pp9 = PdfPages('99.pdf')\n", "pp9.savefig()\n", "pp9.close()\n", "comp_fig9.savefig(\"99.png\")\n", "show()\n", "display(FileLink('99.png'))\n", "\n", "# station 10\n", "\n", "comp_fig10 = pl.figure(figsize=(40, 10),dpi=600, facecolor='w', edgecolor='k')\n", "comp_fig10.suptitle('PS10', fontsize=30, fontweight='bold')\n", "\n", "comp_ax_10 =comp_fig10.add_subplot(1,3,1)\n", "comp_ax_w_10 =comp_fig10.add_subplot(1,3,2)\n", "comp_ax_f_10 =comp_fig10.add_subplot(1,3,3)\n", "\n", "comp_ax_10.grid(True)\n", "comp_ax_10.set_xlabel('x/c', fontsize=20, fontweight='bold')\n", "comp_ax_10.set_ylabel('Cp []', fontsize=20, fontweight='bold')\n", "comp_ax_10.set_title('Slat', fontsize=20, fontweight='bold')\n", "comp_ax_10.axis([-0.001,1.01,1.5,-4.])\n", "\n", "comp_ax_w_10.grid(True)\n", "comp_ax_w_10.set_xlabel('x/c', fontsize=20, fontweight='bold')\n", "comp_ax_w_10.set_ylabel('Cp []', fontsize=20, fontweight='bold')\n", "comp_ax_w_10.set_title('Main Wing', fontsize=20, fontweight='bold')\n", "comp_ax_w_10.axis([-0.001,1.01,1.5,-4.])\n", "\n", "comp_ax_f_10.grid(True)\n", "comp_ax_f_10.set_xlabel('x/c', fontsize=20, fontweight='bold')\n", "comp_ax_f_10.set_ylabel('Cp []', fontsize=20, fontweight='bold')\n", "comp_ax_f_10.set_title('Flap', fontsize=20, fontweight='bold')\n", "comp_ax_f_10.axis([-0.001,1.01,1.5,-4.])\n", "\n", "comp_ax_10.plot(cp_ps10_slat[0],cp_ps10_slat[6],'b.', color='b', markersize=25, label = 'Experiment')\n", "comp_ax_10.plot(s_norm_loop_10,s_loop_data_10[2][1],'b.', color='r',markersize=20, label = 'zCFD') # \n", "comp_ax_w_10.plot(cp_ps10[0],cp_ps10[6],'b.', color='b', markersize=25, label = 'Experiment')\n", "comp_ax_w_10.plot(w_norm_loop_10,w_loop_data_10[1][1] ,'b.',color='r',markersize=20, label = 'zCFD')\n", "comp_ax_f_10.plot(cp_ps10_flap[0],cp_ps10_flap[6],'b.', color='b', markersize=25, label = 'Experiment')\n", "comp_ax_f_10.plot(f_norm_loop_10,f_loop_data_10[0][1], 'b.', color ='r', markersize=20, label = 'zCFD')\n", "\n", "comp_ax_10.legend(loc = 'upper right',numpoints=1,prop = prop)\n", "comp_ax_w_10.legend(loc = 'upper right',numpoints=1,prop = prop)\n", "comp_ax_f_10.legend(loc = 'upper right',numpoints=1,prop = prop)\n", "\n", "pp10 = PdfPages('10.pdf')\n", "pp10.savefig()\n", "pp10.close()\n", "comp_fig10.savefig(\"10.png\")\n", "show()\n", "display(FileLink('10.png'))\n", "# station 11\n", "\n", "comp_fig11 = pl.figure(figsize=(40, 10),dpi=600, facecolor='w', edgecolor='k')\n", "comp_fig11.suptitle('PS11', fontsize=30, fontweight='bold')\n", "\n", "comp_ax_11 =comp_fig11.add_subplot(1,3,1)\n", "comp_ax_w_11 =comp_fig11.add_subplot(1,3,2)\n", "comp_ax_f_11 =comp_fig11.add_subplot(1,3,3)\n", "\n", "comp_ax_11.grid(True)\n", "comp_ax_11.set_xlabel('x/c', fontsize=20, fontweight='bold')\n", "comp_ax_11.set_ylabel('Cp []', fontsize=20, fontweight='bold')\n", "comp_ax_11.set_title('Slat', fontsize=20, fontweight='bold')\n", "comp_ax_11.axis([-0.001,1.01,1.5,-4.])\n", "\n", "comp_ax_w_11.grid(True)\n", "comp_ax_w_11.set_xlabel('x/c', fontsize=20, fontweight='bold')\n", "comp_ax_w_11.set_ylabel('Cp []', fontsize=20, fontweight='bold')\n", "comp_ax_w_11.set_title('Main Wing', fontsize=20, fontweight='bold')\n", "comp_ax_w_11.axis([-0.001,1.01,1.5,-4.])\n", "\n", "comp_ax_f_11.grid(True)\n", "comp_ax_f_11.set_xlabel('x/c', fontsize=20, fontweight='bold')\n", "comp_ax_f_11.set_ylabel('Cp []', fontsize=20, fontweight='bold')\n", "comp_ax_f_11.set_title('Flap', fontsize=20, fontweight='bold')\n", "comp_ax_f_11.axis([-0.001,1.01,1.5,-4.])\n", "\n", "comp_ax_11.plot(cp_ps11_slat[0],cp_ps11_slat[6],'b.', color='b', markersize=25, label = 'Experiment')\n", "comp_ax_11.plot(s_norm_loop_11,s_loop_data_11[2][1],'b.', color='r',markersize=20, label = 'zCFD') # \n", "comp_ax_w_11.plot(cp_ps11[0],cp_ps11[6],'b.', color='b', markersize=25, label = 'Experiment')\n", "comp_ax_w_11.plot(w_norm_loop_11,w_loop_data_11[1][1] ,'b.',color='r',markersize=20, label = 'zCFD')\n", "comp_ax_f_11.plot(cp_ps11_flap[0],cp_ps11_flap[6],'b.', color='b', markersize=25, label = 'Experiment')\n", "comp_ax_f_11.plot(f_norm_loop_11,f_loop_data_11[0][1], 'b.', color ='r', markersize=20, label = 'zCFD')\n", "\n", "\n", "comp_ax_11.legend(loc = 'upper right',numpoints=1,prop = prop)\n", "comp_ax_w_11.legend(loc = 'upper right',numpoints=1,prop = prop)\n", "comp_ax_f_11.legend(loc = 'upper right',numpoints=1,prop = prop)\n", "\n", "pp11 = PdfPages('1_11.pdf')\n", "pp11.savefig()\n", "pp11.close()\n", "comp_fig11.savefig(\"1_11.png\")\n", "show()\n", "display(FileLink('1_11.png'))\n", "\n", "\n", "\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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jhu++vlBpZ3Ky8m4mmD8fPvvZUF6o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\nHTUHMgcyAwLMgYJMOfBu2eKri/XERz4Cr7ySfdxll8GPfpT78WtFLu2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Hp5VIQHc3bNgA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AAABcCoUK3YPCiESkiy9O3+7CBa48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jnHjrlXy/GamFOD7a4A1BbmEwACMX++9MUvmi8n2WMou3ZdoZDU0GDuy5yHZo7JOjpcK/mErrxC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\n48f6wHwCQ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GdcZsyQbrqpqq74AYCs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b35AcR6dO+XsLT+Pjcamjw31MR0fpiU9VwPkC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Ur/fWNAO3aUe8Sohob/3oArxAEk4gCGOICUOw7Yl2gu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\nHy89+qj83y0bVLqKRl6ST0+PJbqcPGnPDRpk/589q8TZs4F/FuXoiBq1X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Vy/kDAAD4gLUECjU0HrysCYJBS6AZHi49plHa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\nV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8YOoFd+C01N0sMP00ILQH50dkp7967c/qf6ir6i+5R2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\nxbBG7VCrlmidZjQmtHvwao15Y3/mF+DtP6TsqnZBUoE/FypRLCa95z3S7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NL1NO1ymjm5TWa+Ro0yZpzx7p3e+uvysAAAAA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\nF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qur08c+9rFzY+gxDKCsjh+Xtm+XHnrIvl+/XrrrLum+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\nK+vc2Cg9+SSVTgGUttpaO3+U7RxSukWd22+3723cOJj4MzBgSbKJhH1dV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1i4UFu4N1vbZskdrbpbY2/88J6r3hC58XkMgB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\nU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D4T3Axa4/n9d1WkCiIOkBeIFRADkIgDGMQB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\nAr29vlZZ61VA5xRQl4aoQj0qU496VK4elSugXkVU1vd8ParQsM9eoCGfG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xtNBSrHy/EJgDBeZAYA4UmANJUfl+ITAHCsyB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\nm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JaEEfYJyEJkfQ8O2gdRaunFVCdO2OPJajwu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\nBrXUDyo6vUFaudLayHhdDQgACC5TZZ/Jk+24W1c3miB57bXSxx9LDz44e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oaXsW6rjQfvy4NDkpnTmz/WV27ZKOHGnBjkAA\nAABoCnMJlCMPAAAAqIfX+JHFOwAAAAi/Ou9EZAyJkqBk4cQJaf9+6dSp6m327GlRSS8AAAA0LCjj\nRwQDebiA3YEAAAB88Ro/9nS4L21B3ThI5ACGHIAMQCIHsZRMSmNj0uysdOyYNDur/KWXcpEQvnT7\nnFEoSPv2eS/ckez5yUlrj9bqdgYQDOQAEjmAIQeQyAHgWyYjDQxI4+PS3Jy0uGi/jo/b8Uym2z1s\nO84XkMgBDDmARA5gGslBJBbvAAAAAEBYzcxULpVVyenT1h4AAAAAus517e6ClZXNZawle7yyYs+7\nbjd6BwAAECqUzQIAAEDsMIZESRCyMDwsPfmk//Z790rcwAMAANAdQRg/Ijhin4eFBdthZ+vCnXKO\nI83P2265AAAAMRf5slkAAAAAEBquaxe5p6el0VF9+ifTukULukzcjQoAAAAgRHI574U7kj2fy3Wm\nPwAAACEWicU71I2DRA5gyAHIACRyAEMO4FdHs5LJSAMDdnfq3Jy0uKjbnp3TN3Wb/j9dr/+p3+tc\nX3AR5wtI5ACGHEAiBzDkAPBhdbW17UKK8wUkcgBDDiCRA5hGchCJxTsAAAAAEHiuKxUK0srKtrtT\nd+i83q7n9G/1mP6n7vJ8md7eNvYRAAAAAPzq729tOwAAgBhLrIe8IGvsa8oCAACgbowhUdLRLCws\n2I47NbaVf1ZX63o9o9eU3Pbc7t3S0aPSTTe1q5MAAAAx5rrS0pKV+FldtQUHExPS0JCUtLEZcwmU\ni2seCgVpZkbavbygv/jRuBx5zHEcR5qfl8bGOtdBAACAgPIaP7J4BwAAALHDGBIlHc3C9LSVyqrh\nnBK6Td/Sd7T54vaePdLBg9LgYLs6CAAAEGOZjK1IWFvbvNjacaS+PimdlrJZ5hLYJI55OH5cmpyU\nzpyRLpOrH2tA12ul+g+kUtLy8sUFcAAAAHHmNX6MRNks6sZBIgcw5ABkABI5gCEH8KtjWVld9dVs\nh9Y1odymY7t2sXCnnThfQCIHMOQAEjmIpQrlTfOl54pFO14oWDsgxk6ckO65xxbuSNJrSqqgtJ5R\nSr+Us7mx49jCnXQ68gt3+NyARA5gyAEkcgDTSA52tr4bAAAAAIBt+vt9N33PFava+x77fW+vdOCA\nXfMGAABAGywt2Y47XtbWrB0QU4WCtG/fxsKdkn3K6jK5GtKSJpRTv1b182S/fvtzE/o3Hx2K/MId\nAACAVqFsFgAAAGKHMSRKOpqFhQXpttuk8+drt52akmZn298nAAAA+C5vqqkpJebmmEvgojjNLScn\npSNH/Lfft086fLh9/QEAAAijyJfNAgAAAIDAGxqSrrqqdrudO6WJifb3BwAAAMZneVPf7YAIOnu2\nve0BAADiLhKLd6gbB4kcwJADkAFI5ACGHMCvjmUlmZQGB6UdO7zb7dplC33QMZwvIJEDGHIAiRzE\nUoXypnmf7QCAzw1I5ACGHEAiBzCN5CASi3cAAAAAIBQefVQaH5euvlpKJDY/t3OnlEpJ6bQt9AEA\nAEBnTExIjuPdxnHYHREAAABA2yTWQ16QNU41ZQEAANAajCFR0rUsuK60tCTlclZ+ob/fvgwaGmLh\nDgAAQKe5rjQwIK2sVG+TSknLy0pcfjlzCVwUp7nl5KR05Ij/9vv2SYcPt68/AAAAYeQ1fmTxDgAA\nAGKHMSRKyAIAAAAkSZmMVChIa2tSsbhx3HGkvj7bHTGbZfyITeKUh0JBuusu6cyZ2m1375aOHpVu\nuqn9/QIAAAgTr/FjJMpmUTcOEjmAIQcgA5DIAQw5gF9kBWQAEjmAIQeQyEFsZbPS8rI0Py9NTSn/\n3vdKU1P2eHnZngdiLJ22nXf27PFut2eP7bgTp4U7fG5AIgcw5AASOYBpJAc7W98NAAAAAEBDKKcF\nAADQPcmkNDZm/+Xz0vBwt3sEBMrgoHTwoJXEqrQDz65d9vzgYOf7BgAAEHaUzQIAAEDsMIZESaCy\n4LNUAwAAALonUONHdF1c81AoSDMz0tmzG8d6e6UDB2zaAgAAgMq8xo8s3gEAAEDsMIZESWCy4LrS\nwIC0slK9TSplJRvYgQcAAKBrAjN+RCCQBwAAANTDa/zY0+G+tAV14yCRAxhyADIAiRzAkAP4FYis\nLC3ZjjteTp+Wjh3rTH9iJhAZQNeRA0jkAIYcQCIHAPzjfAGJHMCQA0jkAKaRHERi8Q4AAAAAhFou\nt7lUViXnzkn//t9beS0AAAAA0eC60sKCND0tjY7arwsLdhwAAACxQdksAAAAxA5jSJQEJgujo9Li\nor+2lM8CAADomsCMHxEITechk5EKBduFs3wxv+NIfX1SOi1ls813FAAAAIEQ+bJZAAAAABBq/f3+\n266tWZktAAAAAOHlurZwZ2Vl+y6cxaIdLxTYgQcAACAmIrF4h7pxkMgBDDkAGYBEDmDIAfzK33RT\n97emn5iwu2v9KBatzBZahvMFJHIAQw4gkQMYcoC2W1qyhfleWLgfCpwvIJEDGHIAiRzANJKDSCze\nAQAAAICG/eAH0tycND4uDQzY1vWdNjRk2+L7tbravr4AAAAAaL9cbvuOO1tFeeG+69oNFNPTVka4\n2zdUAAAAdFliPeQFeqkxDAAAgHoxhkRJIpHQtiSkUtLyspRMdrYzmYz07W9Lr7xSu+3UlDQ72/4+\nAQAAYBPmEijXVB5GR6XFxdrtRkakY8cae4+gymSsJNja2uYFTI5jNzWk01I2273+AQAAtInX+JGd\ndwAAAACgXLe2ps9mpUcekXpqTNMSCen0ae5KBQAAAMKsv7+17cLCdW3hzsrK9p2HikU7Xigw1wEA\nALETicU71I2DRA5gyAHIACRyAEMO4Fd+64Fubk0/Olr74vz6ut15280yXxHD+QISOYAhB5DIAQw5\nQNtNTNhOM14cx9pFydKS3TDhpVs3VDSI8wUkcgBDDiCRA5hGchCJxTsAAAAA0FKrq91532TStohP\npWpfyOeuVAAAACC8hoasRJSXvj5rFyW53PYdd7YqFqU//MPO9AcAACAgEushL9BLjWEAAADUizEk\nShKJhComYWpKmp3tdHc2uK7daXrffdJ3vuPd1nGk+XlpbKwzfQMAAIgx5hIo13QeMhlbjL+2tnlB\ni+PYwp102srrRsnoqLS4WLvdpZdKP/+53eAAAAAQEV7jx50d7gsAAAAABN/tt3f3/ZNJW4zjp3xX\nqcwXi3cAAACAcMlmNxbu53K2A2h/v5XKGhqK5sKVWmWCS/71X+3vhXkOAACIiUiUzaJuHCRyAEMO\nQAYgkQMYcgC/8t3ugBe/5bu6VeYrIjhfQCIHMOQAEjmAIQfomNLC/dlZ6dgx+3VsLJoLdyRbmNTj\n46up9XV/NzMEAOcLSOQAhhxAIgcwjeSAnXcAAAAAYKvHH5c+9KFu98L/Xal+25UpFKSZGens2Y1j\nvb3SgQO2Oz8AAAAAtNzQkHTJJdLrr9duy00KAAAgRhLrIS/QS41hAAAA1IsxJEoSiYQqJmFkxO56\n7baFBWl83EpjVeM40mc+I62s+N5m//hxaXJSOnNm+3O7dklHjkiDg635IwAAAEQJcwmUIw8Nuu46\nm7/UMjVlOxEBAABEhNf4kcU7AAAAiB3GkCipungnKBeJXVcaGPC+sL1jh5RISG+8sXHMcaS+PttC\nJ5vd1PzECWn/funUqeovuWePdPAgC3gAAAC2Yi6BcuShQQsL0h13bJ7DbOU40vy8lRADAACICK/x\no4/CosFH3ThI5ACGHIAMQCIHMOQAfuW3HnAc27kmCJJJW4CTSlm/yjmOLdw5d277Re9i0Rb8FAq2\nAOiCQkHat8974Y5kz09OWvs44HwBiRzAkANI5ACGHABtNDRkW3566euzdiHA+QISOYAhB5DIAUwj\nOYjE4h0AAAAAaJmgXSTOZqXlZbvrdGrKSnpNTVmprETC+2dXV6WZmYsLeGZmKpfKquT0aWsPAAAA\nAC1V6yaFVMqer1IGGAAAIIoomwUAAIDYYQyJkk1lszxKTQXS9LQ0N1e7XSIhXXutlE5r+PmsnnzS\n/1vs3StxsxAAAIgN15WWlqRczhZB9/fbjoxDQxcXETCXQDny0CQf/88BAABEidf4cWeH+wIAAAAA\nwTIyEs6LxKur/tqtr1sJrdde01uvf1HSle3sFQAAQDhlMlYzdG3NSpCWHDoUrgXeQJgkk9LYmP0H\nAAAQc5Eom0XdOEjkAIYcgAxAIgcw5AB+5e+9V5qdtQvGYVm4I9mCo3o895weLAzoiDLt6U+Icb6A\nRA5gyAEkchBLrmsLd1ZWLi7cyZeeKxbteKFwsRQpAJTjcwMSOYAhB5DIAUwjOYjE4h0AAAAAiJ2J\nCSv1VYdfe+N53ayCLpO/L556exvpGAAAQMgsLdmOO17W1qwdAAAAALRBYj3kBVmpKQsAAIB6MYZE\nSaiz4LrSwIDdCV6HX8rRuOb1HXlvTb97t3T0qHTTTU30EQAAIAymp6W5udrtpqaUmJsL7/gRLRfq\n+QQAAAA6zmv8yM47AAAAABBGyaSUTkuplNTjf2p3iYp6aOeUvqpp3aKFirvw7NkjHT7Mwh0AABAT\nq6utbQcAAAAAdYrE4h3qxkEiBzDkAGQAEjmAIQfwK9RZyWal5WXps5+VEgnfP/b2N/5J05rTvMb1\nYw3oiDIXn9u1Szp4UBocbEeHgynUGUDLkANI5ACGHMRQf/+2Q3mf7QCAzw1I5ACGHEAiBzCN5CAS\ni3cAAAAAILaSSenAAenaa+v+0UtU1PVa0dCbChr9gKt9+6xUVpwW7gAAAGhiovZC6ETC2gEAAABA\nGyTWQ16QlZqyAAAAqBdjSJREKguZjLS0JD33XP0/29Mj/dZvSe99r30pNTRki4LarFCQZmaks2c3\njvX22lqkdLrtbw8AAGBefFF6+9ulc+eqt9mxQ3r2WSWuuio640c0LVLzCQAAALSd1/iRxTsAAACI\nHcaQKIlcFl58URoYkJ5/vvHXcBypr89Wz2SzrevbFsePS5OT0pkz25/btUs6cqTCDkCuawuUcjlp\nddVKV3RwsREAAIiohQXp1lslr3FhIiF961tK/O7vRmv8iKZEbj4BAACAtvIaP0aibBZ14yCRAxhy\nADIAiRzAkAP4FamsXHmltHevlErZIpxGFIvSyopti+O6rezdRSdOSPfcU3nhjmTH9++3dhdlMrYw\naXxcmpuTFhft19tuk66/Xvq932u4P5HKABpGDiCRAxhyEEO53LaFO/mtbdbXrR0AbMHnBiRyAEMO\nIJEDmEZyEInFOwAAAACAC7JZaXlZmp+Xpqakd7yjsddZW7NdblqsUJD27ZNOnfJud+qU7cxTKMgW\nERUKtqioWNzc8Px5KxX22GPSXXe1vL8AACAGVldb2w4AAAAA6kTZLAAAAMQOY0iUxCILCwu2W83W\nRS9+vOMd0u/+bktLU01OWkksv/btkw5/2Oef4eqrpWeeoYQWAACoz/S07ehXy9SUEnNz0R8/wrdY\nzCcAAADQMpEvmwUAAAAAqGJoSOrra+xn/+mf7Ius8XErWZXJNN2ds2cbaJ/L+Vt89MILbdktCAAA\nRNzERO2So45j7QAAAACgDSKxeIe6cZDIAQw5ABmARA5gyAH8inxWkkkpnZZSqdpfSlVTLFrJqqNH\npd5e6ZZbpK9/3cpZdYLfEhXr69LHP267DdXRt8hnAL6QA0jkAIYcxFCFxc75rW36+qwdAGzB5wYk\ncgBDDiCRA5hGchCJxTsAAAAAAA/ZrLS8LM3PS1NT0o03SolE/a9z7pz07LPSsWPSnXdKV13Vkt14\naurv99/2pz9t6U5BAAAgBrwWOzuOHU+nKc0JAAAAoG0S6yEvyEpNWQAAANSLMSRKYpsF17XFLSsr\nzb9Wf7/093/v+8usyUnpyBH/L79vn3T4wwvSbbdJ58/X17dUyhYt8UUbAADww3WtBGcuZzv/9fdb\nqayhoYvjidiOH1EReQAAAEA9vMaPLN4BAABA7DCGREmss5DJSIWCtLZmZbGaccMN0nvfu+3LrUoK\nBemuu6QzZ2q/7O7dVqnrpj2udP310nPP1dcvx7HdhsbG6vs5AACAKmI9fsQ25KGLfCy2AwAACBqv\n8WMkymZRNw4SOYAhByADkMgBDDmAX7HNytZSWu94R+OvdfKkNDfnq1xVOm077+zZ4/2Sv/3rrr71\n8QXddP+09KEPSZdeWn+pr2LRLubXENsMYBNyAIkcwJADSOQACLRMxuYd4+M2D1lc9D0faQfOF5DI\nAQw5gEQOYBrJwc7WdwMAAAAAEArJpO1KMzYmLSzYxe5mduEpFq0U14svSh/8oHTddRXvfh0clA4e\ntJJYlXbgmb8so5GXC7r0/96yK1AiIfX01Fc+a3W18T8PAAAAgGBxXdvOs1IJ4NJ8pNSOHXgAAECI\nUDYLAAAAscMYEiWJRELrIyNssS7Zxe2BgcoXwZvhOFJfn225k81ueqpQkGZmpJfOuHrPPy9p7ws5\nXXv+Gf36vzylna+9Wv01r7lGestbpH/4h9rvPzUlzc42+YcAAAAwzCVQjjx0gZ+bDiifCwAAAspr\n/MjOOwAAAADibXHRfj10qOoik1hIJu3PLklra83twFOudPfrmTPS294m/dqvSb/zO9L580o//7wO\n/+xntnDopZekN97w95ovvST9x/8o/dmf1b5oPzHRkj8GAAAAgADI5WrPVUrlc1m8AwAAQqSn2x1o\nBerGQSIHMOQAZAASOYAhB/ArX/pNaZFJoWCLSeIom5WWl+0u1bvvli69tHWv/cYb0i9+YbvlPPKI\n9N//uy2cWlmRnn/e/8Idyf6tfvYzW2zlpa/PdlOqgfMFJHIAQw4gkQMYcgAElN+yuB0sn8v5AhI5\ngCEHkMgBTCM5iMTiHQAAAABombU1aWmp273onmTS7lB9+GHpjjuCW0bs7FnbKSiVsh12yjmOHU+n\ng9t/AAAAAPXr729tOwAAgIBIrIe8ICs1ZQEAAFAvxpAoSSQSqpiEqSlpdrbT3Qkm15WOHZPuv1/6\n0Y+k11+3slXdVvo3cl1bbJXL2d21/f1WKmtoiIU7AACg5ZhLoBx56IKFBWl8vHb53Pl5ymYBAIDA\n8Ro/sngHAAAAscMYEiVVF++MjNiCFWznutLAgJW76hYuxgMAgC5hLoFy5KEL/MxHUikrB8xifgAA\nEDBe48dIlM2ibhwkcgBDDkAGIJEDGHIAv/KVDrLFenXJZPVyVZ3S12c767QI5wtI5ACGHEAiBzDk\nAAgor/lIl8rncr6ARA5gyAEkcgDTSA52tr4bAAAAABBijmNll1BdNru5XNUzz0hPPSW9+mp739dx\nbOFOhy/GAwAAAAiQrfMRyucCAIAIoGwWAAAAYocxJEoqls1ii/XGZDJSoSCtrUnFYute981vlm6+\nWbruOi7GAwCArmMugXLkAQAAAPXwGj+y8w4AAAAASOzq0qytd7/+wz/Y7xv9MqP83yObbW1fAQAA\nAAAAACBAerrdgVagbhwkcgBDDkAGIJEDGHIAv/Lvfa80NSXNz9uOOywUaVwyKY2NSbOzUj5vi292\n7Kj9c44jXXON7Xo0MtLxfw/OF5DIAQw5gEQOYMgBAL84X0AiBzDkABI5gGkkB+y8AwAAACDevvQl\naXi4272IptOnpRdflO6/X3rkEfv9294m/c7vSOfPS88/L/X3t74cVvkOQKur7XkPAAAAAAAAAGiR\nxHrIC7JSUxYAAAD1YgyJErIQQZmMVChIa2tSsbhxnDJcAACgmjoW/jJ+RDnyAAAAgHp4jR9ZvAMA\nAIDYYQyJErIQMa4rDQxIKyvV26RSVo6LHXgAAIBU98Jfxo8oRx4AAABQD6/xY0+H+9IW1I2DRA5g\nyAHIACRyAEMO4BdZiZClJfvizcvamrUrQwYgkQMYcgCJHMSK69rCnZWVzQt3JOWLRTteKFg7oBmu\nKy0sSNPT0uio/bqwQLYigs8NSOQAhhxAIgcwjeRgZ+u7AQAAAABAF+Ry275426ZYtHZjY53pEwAA\nCK56Fv4ydkCjqu3udOgQZV0BAABwEWWzAAAAEDuMIVFCFiJmdFRaXKzdbmREOnas/f0BAADBNj0t\nzc3Vbjc1Jc3OSmL8iM1q5oGyrgAAACgT+bJZAAAAAACov7+17QAAQLStrra2HbBVg2VdAQAAED+R\nWLxD3ThI5ACGHIAMQCIHMOQAfpGVCJmYkBzHu43jWLsyZAASOYAhB5DIQax4LOjN+2wHeKqnrCtC\ni88NSOQAhhxAIgcwjeQgEot3AAAAAKBhn/qUlUxYWLBt7RFeQ0NSX593m74+awcAANDgwl/AN3Z3\nAgAAgE+J9QAX6P30pz+txx9/XJdccone+c536sEHH9Sv/MqvbGpDjWEAAADUizFk9PmZS0gXslB6\n4Di2sCOdlrLZjvYXLZTJSIWClR8ov8uZf18AALCV60oDA9LKSvU2qZS0vCwlk5KYS8RFXfMJrzxM\nT0tzc7XfcGpKmp1toscAAAAIA6/xY6B33hkdHdXy8rL+9m//Vu9+97v1+c9/vttdAgAAABAC+NNC\nDAAAIABJREFUDc0likX74qZQYAeeMMtm7Qu2+Xn7EmRkxH7NZqX/+l+lt75VGh1ltyUAAGALctJp\nW6CzdQcex7Hj6fTFhTuIj5Z9N8HuTgAAAPAp0It3RkZG1NNjXXzf+96nM2fOVGxH3ThI5ACGHIAM\nQCIHMOQg3vzOJSQpv/XA2pq0tNS2vqEDkklpbMzuXj52TPpf/0v6T//JduWZm5MWF+3X8XFpYED5\n4eFu9xgBwOcGJHIAQw5ipsrC3/yf/7kdZ8e+WKpnPuGJsq6xwOcGJHIAQw4gkQOYRnKws/XdaI8H\nHnhAk5OT3e4GAAAAgJCpey5RLEq5nC3+QPi5ru2mVKkcRmm3pddes3bcVQ8AQDyVFv6Wj//yecYG\nkNTkdxOl3Z0k77KuZK3zXNdu2sjlpNVVqb/fdkAaGuLfAwAAdEXXF++MjIzo2Wef3Xb8c5/7nO64\n4w5J0l/8xV/okksu0e///u9XfI2HHnro4sqlK664QjfccIOGL9w5WTrO4+g/Hh4eDlR/eNy9xyVB\n6Q+Peczjzj8uHQtKf3jc/ccnT57Uyy+/LElaqfQFPkKpFXMJSXpIUv7C76+QdIOk4dVVScHIL4+b\nfPz00xpeW7PHMsMXfr34+KWXpKUl5S+9tPv95XHXHpeOBaU/POYxjzkf8DgYj++77z6dPHlSqVRK\niJZWzSc+8pGPXMxHxe8nPvYxDd98s403v/xl6bnnNHzDDdLEhPKSdOmlGr7wWt3Oe2we/7f/JhUK\nyp8+LZ07t/H3/9BD0pVXanhoSMpmg9NfHoficelYUPrDYx7zmPMBj4PxuJ75RGJ9fX29Zqsueuih\nh/TVr35V3/3ud3XphQup5RKJhAL+RwAAAEDAMIaMh1pzCelCFio9MTVlJZcQftPTViKrFv7NAQCA\nD8wl4sP3fII8hIvrSgMDlXfmLEmlrGweO/AAAIAW8xo/9nS4L3VZWFjQF7/4RT322GNVB8fSxuol\nxBs5gEQOQAZgyAEkchB3fucSkpTfesBxbLt0RMOFXZS85H22Q7TxuQGJHMCQA0jkIO7qmU8gZJaW\nrISZl7U1a+cT5wtI5ACGHEAiBzCN5CDQi3f+6I/+SK+88opGRkZ044036mMf+1i3uwQAAAAgBJqa\nS/T1SUND7escOqu/v7XtAAAAEHl8NxFhuZxULHq3KRatHQAAQAcFvmxWLWxLCQAAgHoxhkTJprJZ\njmMLd9JpKZvtZrfQSgsL0vi49wV6x5Hm56Wxsc71CwAAhBJzCZQjDyE0OiotLtZuNzIiHTvW/v4A\nAIBY8Ro/7uxwXwAAAAAgWEZGbNeViQnbcSeZ7HaP0EpDQ7Yoa2Wleps3vUn64hft7lpyAAAAAEQX\nO3MCAICACnTZLL+oGweJHMCQA5ABSOQAhhzAr/y990qzs7brCgs2oieZtN2UUinbYaeC/CuvSN/7\nnjQ3Z7v0DAxImczF5wsFaXJSGh7e+G9y0o4jOvjcgEQOYMgBJHIARNbERNV5wUWOY+184nwBiRzA\nkANI5ACmkRyw8w4AAAAAINqyWcl1paUl213nmWekp56SXn11e9ticWOXHtfV8b9JanJSOnNme9Pj\nx6UjR6TBwbb2HgAAAECr+NmZs6/P2gEAAHRQYj3kBVmpKQsAAIB6MYZECVmIqYUF22GnWKzexnH0\n48/P6/96YEynTlVvtmePdPAgC3gAAIgLxo8oRx5CKpOxbTTX1jbPCRzHFu789m9LH/2oLfxfXaXM\nMgAAaBmv8SOLdwAAABA7jCFRQhZianraSmTVcDg5pd93Z2u2271bevRRq84FAACijfEjypGHECvf\nmbN8gc7Bg9IPf1h9YU86bTt7AgAANMBr/NjT4b60BXXjIJEDGHIAMgCJHMCQA/hFVmJodXXTw3yV\nZiPu1/RVTesWLegyuVVf7vRpaWamdd1Dd3AugEQOYMgBJHKAFnBd2/FxeloaHbVfFxbsOLovmZTG\nxqTZWenYMft1aMgW7qysbN+ls1Ret1DY9m/I+QISOYAhB5DIAUwjOdjZ+m4AAAAAXVJ+59zJk9IN\nN7C1NYDt+vt9NbtSL2lac/oDPagXdJWOa1D/To9WbHv2bCs7CAAAgFCrVpbp0CF2bwmypSX7N/Oy\ntmbtxsY60ycAABAblM0CAABANNSqWV92cZQxJErIQkwtLEjj49vvpq2hqB16TOP6dzq67bm9eyVu\nrAIAIPoYP6JcxTy4rjQwYLu0VJNKScvL3GQSND7L62pqynbqAQAAqFPky2YBAAAg5lzXFu7UubU1\ngJgaGrJFfXVydE4f0AnPEloAAACIuXp2b0GwbCmv23Q7AACAOkRi8Q514yCRAxhyADIAiRzEUoWL\no/mtbbg4iio4Z8RQMmm7caVSkuNsP194uEovaEjbzyW9va3qHLqFcwEkcgBDDiCRAzQhl6u9w2Ox\naO0QLD7L625tx/kCEjmAIQeQyAFMIzmIxOIdAAAAxBwXRwHUK5u1UgXz89Ktt0pve5uvH9uhdf2l\nPq5btHBxB57du6VPfrKdnQUAAEBosHtLeE1MWOltL45j7QAAAFossR7yAr3UGAYAAIBGR6XFxdrt\nRkakY8cYQ+IisoCLpqeluTnfzX8pR2fUp5+8Ja23fjurwcE29g0AAAQG40eUq5gHv+PKqSlpdrY9\nHUNjXFcaGLDS29WkUnYTQDLZqV4BAIAI8ZpPsPMOAAAAwq/Bra0B4KKJCanH/xT5EhV1vVb0f1xe\n0OCNbhs7BgAAgFBh95bw2lJedxPHsePpNAt3AABAW0Ri8Q514yCRAxhyADIAiRzEUoWLo/mtbbg4\niio4ZyCfz0tDQ9JVV9X9s5f+fE1aWmp9p9BxnAsgkQMYcgCJHKAJQ0NSX593m74+a4fgKS+vOzVl\nO/hOTdnj5WV7fgvOF5DIAQw5gEQOYBrJwc7WdwMAAADosNLFUa+trbk4CsBLMikNDkqPPSadO+f/\n54pFKZeTxsba1zcAAACER2n3FklaW7PxYonj2NyU3VuCLZm08T1jfAAA0EGJ9ZAX6KXGMAAAACRJ\nmYxUKHhfHL1whxxjSJSQBWxz113SiRPSCy9IfrPxtrdJd95pu3sNDfFFDAAAEcb4EeU88+C6tkNj\nLietrloZZ8aL0cC/LQAAaJDX+JHFOwAAAIgOnxfQGEOihCygotK55OMfl376U/8/V2GxIAAAiBbG\njyhHHmKojhuHAAAAtvIaP/Z0uC9tQd04SOQAhhyADEAiB7FW2tp6dlb5e++VZmftMXe+wQPnDGzL\nQOlc8pd/aRfh/SoWrXxfoWALgBAqnAsgkQMYcgCJHACowHVtrL+ysmnhTl5iLgA+NyCJHMCQA0iN\n5SASi3cAAAAAAGi5oSG7e7Zeq6vS4KA0PS0tLHDxHgAAAIiCpSXbccfL2pq1AwAAqBNlswAAABA7\njCFRQhZQU7Vt8f1i+3wAACKF8SPKkYeYmZ6W5uZqt5uasp2AAQAAtvAaP+7scF8AAAAAAAiPbNZ2\nzllaknI56Wtfk156yf/Pl7bPl+x1KOMHAAAAhNPqamvbAQAAlIlE2SzqxkEiBzDkAGQAEjmAIQfw\ni6ygZgaSSWlszO6evfPOxt6EUlqBx7kAEjmAIQeQyAGACvr7Kx7O+2yHaONzAxI5gCEHkBrLQSQW\n7wAAAAAA0BETE1YKq17nz0snT9o2+7ffLu3ZYyW5AAAAAISDn7mA41g7AACAOiXWQ16QlZqyAAAA\nqBdjSJSQBdTNdaWBgY1SWM1IJqUXXqCUFgAAIcL4EeXIQ8z4mQukUtLyMmN8AABQkdf4kZ13AAAA\nAADwK5mU0mm7KN/IDjzlXFc6dqwl3QIAAADQZl5zAcex4+k0C3cAAEBDIrF4h7pxkMgBDDkAGYBE\nDmDIAfwiK6g7A9ms3U07Py9NTUk33iglEo29+dSUND0tLSzYYh50DecCSOQAhhxAIgdokOvauG56\nWhodZZwXRVvnAiMjyt96qz1eXrbnEUt8bkAiBzDkAFJjOdjZ+m4AAAAAABBxyaQ0Nmb/NVNK66WX\npLk56dAhqa/P7tTlgj8AAED4ZDJSoSCtrUnF4sZxxnnRUz4XkKR8Xhoe7maPAABABCTWQ16QlZqy\nAAAAqBdjSJSQBbRMtS9r6vXmN0s33yxdd500MSENDbHtPgAAAcL4EeUu5sHPYu5UynZmYWwHAAAQ\nW17zCRbvAAAAIJxcV1paknI5aXVV6u/3/UU3Y0iUkAW0VPl56Wtfs111muE43KUNAEDAMH5EuYt5\nWFiQxse9F3E7jpVWKu3WAgAAgNjxmk/0dLgvbUHdOEjkAIYcgAxAIgexkMnYXY3j41ZuZnHRfh0f\nt+OZDDmAb2QFLctAafv82Vk7JzWrWLS7t7/xDekjH7EvhVy3+ddFRZwLIJEDGHIAiRygTrlc7d0X\ni0Vrh8jhfAGJHMCQA0jkAKaRHOxsfTcAAACANnJdK01TaTvy0hfdkvT6653sFQBsNjpqi3lasdjm\n9delhx+WHnlEuuoqaXBQevTRmj9WKEgzM9LZsxvHenulAwdsMx8AAAC0yOpqa9sBAAAgdiibBQAA\ngHBpwXbkjCFRQhbQVpmM9NRT0pkz0vnzrXvdHTvsPHj0aNUmx49Lk5P21lvt2iUdOWJrgAAAQH0Y\nP6LcxTxMT/vbeXFqynZpBAAAQCxFvmwWAAAAYoTtyAGERTYr/f3fS9/8pn1Rc+ONUiLR/OueOyc9\n8YSV0hodtS+LykpqnTgh3XNP5YU7kh3fv9/aAQAAoAUmJuwmEi+OY+0Qb65rY/fp6YpjeQAAEF+R\nWLxD3ThI5ACGHIAMQCIHkedzm/H8yZNt7giignMG2pqBZNJ2AZudte1wrr22Na/78stWSmtx0e7y\nHh+XBgb08/8zo337pFOnvH/81CnbmadQaE13ooBzASRyAEMOIJED1GloSOrr827T12ftEDm+zxeZ\njDQwYGP3ubltY3llMm3tJ9qLzw1I5ACGHEBqLAc7W98NAAAAoI36+/21u+aa9vYDAOqVTErptP1+\nba32LmL1KBallRWde176uetKStb8kdOnpZkZ6fDh1nUDAAAglrzGeY5jC3fSaWuHeHJdWzm/srL9\nuQtj+YvtyAkAALGUWA95gV5qDAMAAMTMwoLdleb1pbfjSPPztttFBYwhUUIW0BWuKy0tWXm/Z56R\nnnpKevXVlrz0OfXopH5LP9R7ldOEljSk1zwW8uzdK3FDGAAA/jF+RLlteSgf562u2s0nExO24w4L\nMuKtBdcyAABA+HnNJ1i8AwAAgHBxXdtOutLdaiWplLS8XPXiKGNIlJAFBEImY3fhtng3nqJ26HVd\npld0ub6p2ysu5mHxDgAA9WH8iHLkAb5NT1uJrFqmpqzkLgAAiCSv8WNPh/vSFtSNg0QOYMgByAAk\nchB5pe3IUym7K62c49jxdFr5p5/uRu8QQpwz0PUMZLO24HB+Xrr77u3ntgY5Oqe36BX16jlNa07f\n1K16TtcoryFdJrcl7xElXc8BAoEcQCIHMOQAgF++zherq/5ezG87BA6fG5DIAQw5gNRYDna2vhsA\nAABAm2WztbcjZ5IEIEySSdsef2xMeuUV6bHHpHPnWvoWO7Sut+gV/e/6vpb1G3pa79PXe7MtfQ8A\nAAAAFfT3t7YdAACIHMpmAQAAIHYYQ6KELCCw7rpLOnFCeuEFqU0ZPb3jWr3+hfv0rv/38cqLIAEA\nwDaMH1GOPMC3hQVpfNy7TK7j2G6cY2Od6xcAAOgor/Eji3cAAAAQO4whUUIWEGhbdxjr7bWL/s8/\n35KXX5eU6OmRzp/fOOg4Ul+flSfMsisPAABbMX5EOfIA31xXGhiQVlaqt0mlrJwuC+kBAIgsr/Fj\nT4f70hbUjYNEDmDIAcgAJHIAQw7gF1lBYDNQKqU1OysdOyY9/LC0d69d1Hecpl8+IW1euCPZncAr\nK1KhYF8wxEhgc4COIgeQyAEMOQDgl6/zRTJpC+QrjeUdx46n0yzcCTE+NyCRAxhyAKmxHOxsfTcA\nAAAAAEBbZLObd+T5m7+RTp5sfWmttTVbMHTppRs7/1BWCwAAAGjc1rE8Y2wAAFCGslkAAACIHcaQ\nKCELCD0/2+836vLLpV/+0nbjKdmxQ7rsMnvu9tv5ogEAEDuMH1GOPAAAAKAeXuNHFu8AAAAgdhhD\nooQsIBIyGStztba2eaFNJziO1NdnW/xns519bwAAuoDxI8qRBwAAANTDa/zY0+G+tEVc68YVCtLk\npDQ8vPHf5KQdj6O45gCbkQOQAUjkAIYcwC+ygtBnIJuVlpel+Xlpakq6+mrbGacTikXb9efb35Y+\n+EFpelpaWLAdgUIm9DlAS5ADSOQAhhwA8Kut5wvXtfH19LQ0Ohrq8XbU8bkBiRzAkANIjeVgZ7Un\nPvvZzyqRSDTTH0nSZz7zmaZfA9sdP24Ldc6cqfzckSPS4GDn+wUAAABIzCeAjksmpbEx+0+yi/m3\n3CL98IfSq69ubrtjh3TuXGvf/5VXpO99z35/6BC78QAAgKYwnwBUfYdNxtsAAERS1bJZPT3Nb8qT\nSCR0rtUXBCu8R9y2pTxxQtq/Xzp1qnqbPXukgwdZwAMAAFBJHMeQncZ8AggI15WWlqRcTlpdlfr7\npdtvlz7+cXvcTqmU7QiUTFbux8SENDRkzwMAEBKMHzuD+QRiz3WlgQHb5bKaa66Rfvxj6corO9Yt\nAADQHK/xI4t3QqZQkO66q/KOO1vt3i09+qgtvgYAAMCGuI0hu4H5BBBw1e7idRzpTW+ynXSa5ThW\nyuuBB6q/F3cMA0DsFArSzIx09uzGsd5e6cCBcFzHZPzYGcwnEHsLC9L4+ObxcyVXXy3t3ct4GgCA\nkPAaP1Ytm1Xyq7/6q3rLW95S1xv+y7/8i37xi1/U9TPNyOfzGh4e7tj7ddPMjL+FO5J0+rS1P3y4\nvX0KijjlANWRA5ABSOQAhhwEA/MJhEEsM5DNVt8N5/XXbXFPrS8KaikWbUJaKFS+Y7hY3Djuul3f\ngSeWOcA25AASOWin48elycnK1zePH5eOHAnOTuLkIBjCMJ8A2nK+yOX8jceff97G2wEYT8cdnxuQ\nyAEMOYDUWA5qLt6599579clPfrKuF/3Sl76kP/7jP67rZ+BP+R0p7WgPAAAAtBLzCSDAkklpbMz+\nK+e6tiOO1xb9fv3oR7bjjpe1NVtEVOpHF0tshX03CAAIshMnpHvuqX5j4pkz0v790sGDwVnAg+5j\nPoHYqqfE7dbxNAAACKWai3cSiUQn+tEUVq5BIgcw5ABkABI5gCEHwcB8AmFABrZIJjdWqmwtdVWv\nV1+t/fPFoi3UGRurXs7r0KG2l9jauXO4apnqoO0GgfbhfACJHLRDoSDt21d7R/FTp2xnnkcf7cCi\nyRqLRclBMIRhPgG05XzR3++/bfl4Gl3D5wYkcgBDDiA1loOqi3fuvvtuJRIJDQwM1P2iv/mbv3nx\n5wEAAADED/MJIOS2ltX6xjdsIc5rr0nnz/t7Dcfxv1PO6qq9X60SW+vr0te/Lj3+eEt35WE3CABo\nr5mZ2gt3Sk6ftvaHD7exQ11cLAp/mE8g9iYm7JzkdyF9PTv1AACAQEqsr6+vd7sTzUgkEnriiSdi\ns4JtctLu9vNr3742T3QDhPqBkMgByAAMOYDknYNEIqGQD4PRInGbT6AyPjd8Kl/M88wz0lNP2YKe\nalIpae9e6eGHa7/21JR9QTE+XvsLip6ezQuIHEd6xztsIc+73133op5CQRd23MlLGvZsu3t3h3aD\nQNdwPoBEDtpheFh68kn/7ffulfL5NnXGdaWBAe/ykKmU8l/5ioYr7GLBXALlyAOkNn1u+DlXlZua\nkmZnW9sH1IXxAyRyAEMOIFXPgdf4safNfUKLHTgg7drlr+3u3VKd5YABAAAAAKgsmbSt+Gdnpe9+\nV7r1Vlug4zib2zmOHU+n7Y6Src9v5Ti20CaX83dn8dadf4pF6R//Ufr+96W5OWlx0X4dH7cvPDIZ\nz5drZDcIAECILS3Zjjte1takv/u7zvQHACoplbK95prabUvjaQAAEGpVd9658847lUgk9Ad/8Ae6\n44476nrR+fl5Pfjgg0okEjp69GhLOlpNHFe2nzhh23WfOlW9zZ49bOcNAABCpnxHhxaWQqkkjmPI\nTmM+AcRErXO3z90NtLwsfehDtvCm1UqvX+WzJFC7QQBARAXmXOu60p13SseO1W5bZRcLxo+dwXwC\nuODFF208/fzz1dvUGO8CAIDg8Bo/7qz2Q4899pgk6QMf+EDdb/jTn/5Ujz32GDVl22Rw0Bbm7NtX\n+e7AXbtYuAMAAEImk7G6JWtrm3ddOHRI6uuzu82y2e71D3VjPgHERGk3ngplRS4+X6oxtfUc7zgb\n5/hk0hb+tMPami0wqtLHN51zdYuWNKGc+rWqVfUrpwktaUiviS9AAKAVenvb296X0pzjH//RX/vV\n1TZ0An4xnwAuuPJKW9FY6ZrJ1vE0AAAItbaUzer0oDgfw1veBgelo0dtAc/evRv/7dtnx+O4cCeO\nOcB25ABkABI5CB3XtYtQKyvby6UUi3a8ULB2dSAH4cV8Ap1GBtosm7U7gefnbReDkRH7dX7ejpcW\nZ05M1C6x1Yhi0XYGqiST0UOFAc1rXP+b5jSqRU1rTvMa1481oCPyLrmF6OF8AIkctMOBA3bDoR+7\nd0uf/GSLO1A+5/C5S0q+HZ9J6AgW7aDT2v654Xc8ja5i/ACJHMCQA0iN5aDqzjsln/rUp/TpT3+6\nrheN1TaRXluES20t/ZBOS4cPN/0yAAAgIgoFaWZGOnt241hvr10oL216EEhLS3b3mJcauyYguJhP\nAJBUe4ceyebKfX3eJbYaVWn3hAtf5Pb+6/b3u0RFXa8VJSRdJnfTDjxt2Q0i5EI7BgHQMem0dOSI\ntH+/dOpU9XZ79tiO4jfd1MI3d107SfndcUeyxaR797awE2gU8wngAj/j6Uo6WKIcAAA0J7FeZSTb\n09P8pjyJRELnzp1r+nVqvUfXBuPVyjs4zsbdgsVi9W0MK62GZiAFAAAacPy4NDlZvaTmkSMB3plv\nelqam6vdbmpKmp1tyVt2dQwZE8wnADSk2jx7xw6pmfNBpc+QhQVpfHz7rm9lfilH45rXd2Rfkuze\nbbvdbvtSOcZz+VCPQQB03IkTtnN4x84Zpc+V1VXp/Hn/P5dK2W4WFc7hjB87g/kE0AJe32FRohwA\ngK7wGj/W3HmnGZEetJZvtbrV1gU7W58r/Yzrbp4AVhtIHTrEQAoAAFR14oR0zz2VL4BLdnz/fruD\nNZBfnlXaDaGZdoiMSM8nAFSWzVZeCHP77dLHP97YZ4Hj2EKarXI5z4U7ku3AM6GcvqOx6rtBxHgu\nH/oxCICOGxy0RZAd2a3L6/qtl1TKOhLxxZdxwHwCsVbrO6xq31MBAICuqbp454EHHmj6xTtVWzaf\nz2t4eLgj73WRn/IOXraWfmAg1bSu5ACBQw5ABiDFKweFQvU7V8udOmV3xT/6aADLV/T3t7bdBXHK\nQRAxn0CYkIGAqVYS4H/8D6mnZ/simVr6+jZKW5fbshAoL2m4wo/3a1W7dlVZgBLjuXwkxiAVcD6A\nRA7aLZ2WDh/uwBs1cv32lltsdVEySQ66LEzzCSCQ5wtKlHdcIHOAjiMHkMgBTCM5qLp45yMf+UiT\n3Yk4H3foeSoW7TVKgyIGUgAAoAEzM7W/NCs5fdrad+RCeT0mJmx3Aq+xVbVdExBYzCcAtFy1XXl+\n8hP7/dmz1csBVFo443NR6M7r+nX0r6osPGnXXD4EZbgiMQbpokKhQzuPRAh/Z/DtxRel+++XvvjF\n+q7fOo70iU8E5jwbd8wngCb5+Q5r6/dUAACgqxLrId87sms1ZUdHpcXF5l5jZEQ6dsx+Pz0tzc3V\n/pmpKWl2trn3BQAAkTE8LD35pP/2e/dK+Xy7etMg15UGBry3s0+lpOXlll1I79oYEoFDFoCIaGSx\ny8KCND5ee/Ho/Hz1LzTaMZevVoarfDFSAMpwRWIM0iXHj9tuRJUWP+3aJR05Qpmxrfg7g2+7d9sK\nr3Pn6v9Zn3MOxo8oRx4QWH6/wyr/ngoAALSd1/ix6s47qKHOsg01X2PLdt1V+W0HAAAQFsnkxu3S\nXl9UcgcsAKCaaqW2vAwN2WeM1+LRaiW3Slo9l49xGa64OHFCuuee6rsWnTkj7d9fpUxbTPF3Bt9e\nfLGxhTuJhHTttcw5AERLm0qUAwCA9unpdgdaId+NW7cmJuzLpEZtLf3AQKppXckBAoccgAxAIgeh\nlM3aXa7z87Y7wciI/To/b8cb2GGAHMAvsgIyEFOlxaOplOQ4ypc/5zh2vNYXua2ey9dThqsZrms7\nD01P213Z09P22HWbe90IaOf5oFCQ9u2TTp3ybnfqlO0yUyi0rSuh0a2/Mz4XQqT8fPb+99e/cKen\nR/qzP6s45yAHAPwK5PnCz3dYlChvqUDmAB1HDiCRA5hGcsDOO43yc4eel613701MSIcO1d6uu5mB\nVCPbiAMAgEDr7W1v+7bxGpdQax0A0CnZ7MZn0pe/bHPyeubKrZ7L53LeryXZ87lc45+X1cpyPfCA\nfYntOPZn/8M/tIU9Vf4OQjsG6aKZmeq7x2x1+rS1P3y4vX0KOv7O4Kna+awe/f3SgQNcGwUQPa3Y\nZRIAAHRUYj3kBVm7WlPWqw59aUVzseivRr3rSgMD3gMpn3WX6+5rpf4AAIBQKBSku+7y96XG7t3S\n0aPSTTc1+abNLggOwLikq2NIBApZANCUVs/lR0elxcXa7UZGpGPH/PZyg5/+lksmpdtuq/i53JUx\nSMgND0tPPum//d69Utxv2OTvDFXVez7bqom5B+NHlCMPCLQAXH8BAACbeY0f2XmnGeV36FX68kry\n/8VWabtuyXsg1cjCHde1AVqlyWyxuHHcdbnLBACAIPIYb6TTSR05Iu3fb+UCLpOrIS26sZWMAAAg\nAElEQVRpQjn1a1Wr6ldOE3rh14f0/8wmddMeV1pow8KbQ4f8XfhhXAIAiJJWz+XbXVLbT1mucq4r\nPfVUxc/ldFqbxiBblcYkH70ipw9e9Yyu+FNHWl+X3nhDuu46dgIGUB/XtUWL998v/ehH0uuvSy+9\n1NhrvfWt0l/9FecgANFX6zsszoEAAARKJHbeeeKJJzQ8PNzYCwRt4NKO/iwsSOPjtbfxnp8PdZmK\nfD7feA4QGeQAZABSF3PQjs9xn3dJnTghvTSS0cBrBe3Smi7RRttfytH5t/fp0p1vSDt3Nn7HVSt2\nF+jguMQrB9wdiZKm5xOIBMYPkJrMQavGAO3+nJyelubm6vuZnh7pm9+s+n4nTkj79m3egeeIMkpr\n+5hkk2bu+G7jtZx2ng/YRaZ+3fo743MhYDIZOw+5bmte7z//Z+lP/7Rms2o5YC6BcuQBUkQ/N4L2\n3VkIRDIHqBs5gEQOYBqZT8R7551m7xxvh2TSLoi1chFNLle77nOxaO1CvHgHAICOqHbx4uBB6Yc/\nbO24oo5dagZvlF6/oqBLX9ve9hIVpWdXpB07pHPnar5W1Yswfu7WX1uzdtXGFIxLAABR1Kq5/NCQ\njRu8Fsr29W3s9luv1dX6f+b8ec/P5cFBK4k1MyOdPSu96ZyroUJBvf+64v26je645/daTqM7JbdR\nb29720cRf2cxtPX/3d5eW8HVqoU7O3ZI/+E/tOa1ACCqgvjdGQAAMVD3zjvf+MY3dP/99+sHP/iB\nfvGLX+iKK65QOp3W/v37NT4+3q5+VtXwyvZW16UPstFRaXGxdruREdt+Fr4VChsXKEt6e6UDBzZ2\nTgcABEizdw157YJz/nzlhTEljYwr6rn7XqrdtpZad/L7vVt/akqana38XEDGJdwd2T2RmU8AQLv4\n3HWvIY3svCPV97nsZ/xSrp6dhPxey7nxxsqLqh3H/pPseKM7ETY4niwUpLvu2rxLUTW7d9uiqJtu\nqt02yvg7ixHXlW65xf7fffXV9rzHjh124e706aZehvFjdzGfANrMz3jrzW+WHnnErvGE/fszAAA6\nrCU776yvr2t6eloPPvjgpuMvvPCCvvWtb+nb3/627r777m3PB1Yr7hwPi/7+1raDJOn4cWlysvIF\npOPHpSNH7A5EAEAFlb70uP12e+7xx9uzHW+zdw3V2gWnlkbGFfXsUuO3H35eq1of/d6t79WOcUls\nRW4+AQDtUmvXmGbGRRMTNvapd8xQz+eyn/FLuXp23PN7Lcd1peefr/xe1frmZyegRseTF/4907mc\n/q53Vd97pV9ffXlCSxrSa9p4n8vkakhL+ugVOX3wqmd0xZ860vq69MYb0nXXbc9ADMpZpNN2fWX/\nfunUqert9uyxjTBZuNOASjvdvOv/Z+/+o+O6y3vff0bypMk4PQ2pk6DoR0QhJrEI+UEG1q1AFhQp\nAlKdJGip8m1JKRIO9K4DXU7P6rlt6M0tlLa0dWFBS+pK/DCrtVGVEERoJhaEsWKvQKYX3FIbSNJG\nkTWxQ0waOMk49VjW/ePxWKPRzJ69Z/b82vN+reUl75mvZr6SH+/57r2f/TxXSk8+KT3zjH9xlXmf\nPXuk73/fEnQiEenaa6V//3fp6afLTqrJ68ILpcsvl97zHqu4s2mT/++BquB4AqgSN+utF1+U3v1u\n+4ygCg8AAL5xnbzz6U9/2nHhu7Kyoi9+8Yu67rrr9OEPf9iXyblVUt+4ZmrZ4ObkXDhs48pR45NG\n1ewfePCgdMcdhe/8WlqyE0u7dpHAU230kQQx0AAKXfTIdxd4ieV418WBh/ZTZbWMclLKusKPZBmv\nKp14U611idgf1JvAHU8gUIgBSHUWB5VoqS25a8uVq6XF2+dyKesSt9/j9lzOc895n8NZ8aNH1Z8v\n4brU9WTO2vcVkt4t6Ve1W0tqV0JRjWlaezWqqBLqUFLnvZCWvptnctlrYyn/mvoLX5AuuEDauNGq\nmDglYZw4Id1zj905f+KEJTXkS3BwO65Cenvt/MrYWP7zMB0dHs6/5CaQ/O//bRU0W1osyWPLFunq\nqxU/cED96bStSzMJVB0d9vv8wQ/sz0svSeefL116qX1/JsnKz5sSCp1ru/FG6Z//2R5/6qn888z8\nu+cm42RvP/ig/RwnT9rvoRCnuMp+vaNHbS6nT1usnDxpP/Pzz0svv5w/qe7QIW+/Ey/KrKpeV58L\nqOvjCSBQ+wu3idhnzpTWAjXAAhUHKBlxAIk4gCklDlwn7/zt3/7tub+3trbql3/5l3XppZfq2Wef\n1aOPPqrls20idu3a1RiL41pcDKsVNyfn2tvL6+3eRD1QE4nCJ4yyHTlilXnuvZcWWgAaVCWSMp0u\neuTjNrGmGD8q7nm9izwfr+uKWlSpqXTiTTXWJahLgTueAIBGFImsHqDmHr8X0tHh7XO5lHWJ2+9x\nu5Yqp33J8nL+hOtS1pMOa9/zlNYvaUEX/Jz0qzecUN93E2r7r/Xj1sisjVdW7E++38fyst0N/+KL\ntm7LlTlP8+yz0qlTa9u+Pv+89JGPSHffvdpaqLPTeoUXG5dKWWu1e+6xpBhJet3rpA9+0JeWGr29\n1hKraOtyp+Sc556z32EqVfiN/EgkyXdTQnbyy803r03wySTfFEp4ef55ey779UOh8uLcq2JxVW+y\n2+BxMTkwOJ4AqsTruaugdLAAAKAOhFZcNmQ9//zzderUKV122WV65JFH9JrXvObcc0888YTe8pa3\n6Mc//rF+7ud+TidPnqzYhHOV3FPWbZ/38XFpctL769ebQsk1bnu6O3Hbc76MO13qybZtVrLZrbEx\nO2cEAA2lUp8bsZg0POw9CSYclmZnSz8R4Mfn/uCgNDdX2vu7ef183Py+Mr8bqbTfbb7XKvR79usz\nv5LrEpdKXkOiZIE7ngCARpZJcPjgB+0CTaGKG5GI9K53eftc9rre87LOc7umK9fAgCWilPLe2es9\nt2u5P/xD6Y/+yP3vrLXVEjecKqWUq7XV1nQ9PWsTd/KNe+c7pW9+s3BSTKaS1Pvfn79l0thY6TcI\nZFcFeuqp1eozaE7XXSe94Q0VrwTO+rE2OJ4AqqSU9VZQrqMBAFAFTutH15V3XvGKV+jZZ5/V7bff\nvmZhLElXXnmlbr/9dv3FX/yFfvEXf7G82VZLFVs21IXp6cq1tfKjmkEDyb7DqxLjAXiXSLi4A7NR\nVfgO1oLvWW6LqUJKrV5TbitLPyrulVvdppR1hdcqNcXGtrY6X3wpVvHG6W59L3e3VnJdgroVuOMJ\nAGhkmWSKp57yf73ptTWXl4p7bs7ltLSUn9SSb91XynrSbZuvL33J2xrZaT3nl+Vl6bd/u/h7LS/b\nms6pmk0qJd1/v5XOyXXokP2bXnqpVab5pV/KX4Vmyxbp1a+WHn3USh1L9vypU6X/jAiWri7rc8+x\nRGBxPAFUiZv1Vq4gdLAAAKAOtLgd+K53vUuSdKrAQXH67Af5zZmyr1UUj8e9f1PmZJKToLVsyJyc\nm5y0E3OTk7Zd7kGt25NRMzOlv0cqZXesTUzYycOJCdvOOjlUUhwgcIiD5nPggHTbbVYRa/9+af/+\nuPbvt+3bbrNzdyVzse+pqNFR6ZJLpFtvlR56SHrmGfuzb589dsklNsZvXpIyvSrnYN7D967bF/jR\nfmpkxC4klKqUdUUmWaa7e/17h8P2eCZZxs3YtjZ3r+Vketruwp6dtTurBgbs6+ysPe72zvxKrUuy\n8JlQXwJ3PIFAIQYgNXEcRCLSLbfYOjeZtD8PPWSPlfK57LQmyeZl/ZHh5lzOJZdIG1zfq7ZOvLU1\nf8J1KetJt+vXEyfcjau2Rx5xN+6nPy0+ximhamXF2ngtLEgPP2zx981vSv/yL9Ljj1uCzz/8g/TR\nj0rf+MbqcVEFE3fiFXtlVEQkIr3pTb4n7jTt50KdqufjCSBQ+ws3661cfrZzb2CBigOUjDiARBzA\nlBIHrs9mfOxjH9NDDz2kz3/+8xoZGVFvb++55w4cOKDPfe5zevWrX60//uM/9jyJmvDrznH4U83A\nSaHWGpk+7VVorQGgPh08KN1xh7S0lP/5pSVp+3Zp1y4p62PLnVrve1Ip6bHHit/B+p3vlFYBx4mX\npEyvlXDKOZgv53v9qLjn5i7y1lYpFFpbqr/cdYWXKjVuxvpR8SaTeBOAinqonsAdTwAACstdkzz1\nlK2JMi2NXvWq0iruuTmXc8MN0ne/677yT65Nm/InXJeynnS7ft20SXr+eW/zrIZy2rEClXDppdLr\nX79afanSVWlRVzieAKoke7119GjxKny56x8qLQMAULLQisuGrG9961t17NgxPf744wqFQnrVq16l\nyy67TMePH9dTTz0lSdqyZYsuvfTSdd/78MMP+zvrLGX3lGUhUb5S+r67lUpZf3Wnk27d3Xanfzn/\nXh7iYNs2q+jh1tiYtXQHkKPM/W8iYZV1CiXuZOvslO6910MLrWrte5zEYtLNNxc/QG5pkb7+dX+T\nKAYHpbm54uMGBqxiihexmDQ87P1CQDhslV1K/Tn9+jctlNSVfbHo/e9nXdEAyl5DwrPAHk8AAKqv\n2LGE05otUw0onS58I1e+JP1S1pNu1r7hsPSHfyj90R+5XyO3tloiVLntwYoJh0ngQf3o6rLqOnVw\nAx/rx9rgeAKoskxr1d/4DemllwqPy17/FDtvxo3YAAA4rh9dJ++0tLjusLXuzZcr2IubxXEdcHsy\nqpSLrpV87QyPC0q3CQMXKKV3b5rXzl+e0SUnuYALrOHDgVxFE+mqse8pxm1ipFRacqQf712ppMx8\n/EiW8usEAom/gcAasvo4ngAAVJXTmk0qbT3ndT3pNuEnkbDvdbtGvuIKS94ppyWtG297m7WxAmol\nF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z9vST6Li5Y0MTJidy/6dBKUNSQyQqGQVsLhyn0OAgAA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\n6Z57zB8ASCMQyTtNTU2qq6vz9uJMD9BeT+LwXUZxgMAgDkAMQCIOYBAHs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7/jzcdtum9llzc3DoUOGNOxC/b9cW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\nx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AAAlL6gHVqCSLdbQbY7McBTSSTvdHR0qKGh\nId+HgTxzjIN0TzTpnuiCJv8E2c/vPl7blpWZuxtPnHDepqJCGjdOOnTIeRsv5eXmZ2Wg7RXnAxAD\nRSqdZM0U+3Z0dKgh3YFkpgeqDHxzjvMBbBErIAZ8SP4smzJFmjFD2rNHeued1J9tbq1rJk+WjhwZ\n2vJJMgk7qeYiXskfPhNpJkZ7Va9ObdEoSQ1qUptGKAstjP28nq22Nrt2xydO2L9+JhYWgyYA+Uy8\n6t8n1TgqYMukjvZ2NUiM14rdfffZb5cijvlcKDC2Le1DoWCtiB0QBwBscb6ARBzAIA4KQCRiKjHe\nfrv02mvmublzpW99y3QwycG8blAc2I5lbbdzk4mbiGmDlTFBzgclkbwDeEr3onA6J7qgyT9B9vO7\nj9e21dVm4ePtt923ufhiU4ot1YK7ZBJzRo0y/3ap/v1qa6W1a90XR/mgAJAPmR6oMvAFABQzm4oo\n0uDkiA0b3CuwpLM45VT9xWcizQhF1aQ2bdF/lyRNU4CFtUwssgV5DT/7ZHrB0G27oAlAfisYOcVA\n0Mo/zc3m9Y4e9ZXw4ydBu6tLuu02k+sWN3mytG7dwNIDMuC55zK7HfLLtprOmDHON9cBAACg9DU3\nS5s3D71eeeCAtGWLmZ8tW5bb1k+57prCTcRFrSSSd8hghJTlOEjnRBc0+SfIfn73sdlWMgk8Xv0N\nvTJZJe9/vwxczOZ8AGIAEnEAgziALWIFwyoGUs1rrrzSfO/RR53H6rYVUaTByRFPPJFeOymvn5Oq\n+kuAJBiTsNNgdlcWWxjbvl429sn0gqHbdkETgPxWMHKKgSCVf07FeMO776b+OW4JP5YVfrZula6/\nOqJzD3Tqv6tN07RP+zRNbWrS33bWa0NrWIsW2b99uDh4MK3thtXnQqFJ9TlVU2PWobzOD7W1Gb0Y\nQRwAsMX5AhJxAIM4yKNIRHr+eedCA/Fttm93ruCaIYPiIGiHlnRwE3FBCHI+KInkHSAn0jnRBU3+\nCbKfn31st7XZJhyWli83f5zwQQEAAABkl9vY/brrUicZ3HXX0NdJTjzwWxFFMtv/6lfptZPykipJ\nJEASTGLCTpua9HVtdG+dFaSFsZugC3VNTdI993i3ziovt3/9TCwsBk0AylT1oSCVf9JI+LGp8LPt\npbA+uqJZWz7qUrV6B8XX17VRPQeq9MZX6rTt8VYtqvVR5p07KlOz/b+YzfMT/Fu5Utq2TTp8ePB5\nraLCtFJ0M22aqQoNAACA4amzU+rp8d6upyd1BddsCdqhBcNSSSTv0D8QUhHEQdDknyD7+dnHZtsi\nytAs+DhA1hEDkIgDGMQBbBErKPgYsL0471YBZPJk6cgR9zvQEiVXGvFbESX+Gq++6m8fv1IlifhM\npOlTpdrUJKlDUoM6Va8eVekcdTvvFKSFsZugC3X19aaVsVfSS3W1/etnYmExaAJQpqoPBan8cyrG\nOxSvwZRCwISfN+7s1HU/rdeWj7pSxtUIRXWOuhX6SHr9b1bqhNpV/olFmXebqj8bNjgnAn3hC9IL\nL5Rm4k9lpXT8uPd2DolvBf+5UIpWrJA2bZJOnBj6vfjvsrxcJ0NlKjs+EO9RVeq9cJXKz6vTmRmO\nW+IAgC3OF5CIAxglHweFfPNAW5v3jS2SGW+mquCaQYPiIGiHFhS9IOeDkkjeAQAAAACgZCQuhj36\nqPTxx9KxY4MvaCZXxvGqABKkook0UGkk6P7Z5FT9xWciTa+q1Kl6SdslSccUVpfqFJJUlVQhJXAL\n48pK83U0mtmFunBYuuQS98SscFj63OfsXz8TC4tBE4D8VjByioEglX/SSPhxFY2q9+dtOveAVC33\nRJ8q9Wj0JxGVy6LM+5Ej3lV/Dh2SJkwYGhvxRKBQyDxOrGgSP7fU1kpr10oPPii9/LJ5jTFjpJkz\nzZ89e6R33pGmTJFmzBh4XCiL9zNnSjt2eG938qRJhtq8OfvHBGeRiKm4kypxJ8HJWEj/a8w/a9yH\n3YPaznVG6nXmG2E9uE20ngMAAPASNAHHR8vgvPCzbpHrNY6gHVow7IRiMa+ao4UtFAqpyN8CAAAA\ncowxJOKIBeRd8uLNnj0mUefoUbsEhpoaadcu8xqNjdlpAbNmjfk7VXstL0uWSE8/nZ3jir/3VItc\nTouKCU5WVKonVqXnTtRplYYuMI5WRPXqVJPadP7IfZp5+TRNuNFjYc1tMU7K3kJdxEebJT+vmc7x\nOv0OEhOAkhd2IxFp9mz7CkZOMdDe7v3/obLSVPmI323Z0mIX42vWSOvXm6+XLJGefNJzl64zFuvl\n96epRd6vf0JSuddG5eXSLbdIP/5x9to+lZebxJZ0PiPLyqQzzpAuukhauNDEw65dUl+fNHKkNGKE\n+frTT83jiy5KL2YT/eY30qpV/vY57TSToBQKmWOKxQb+HSRp1CjzJ570lOr9fuYz0nnnmffz3HPm\nXD5+vHT11Sa2H398cDLU3LnS6tXDa8E+1bmlpkb653+2irf1WqMbtD7l92bNku64I/sJPIwfkYh4\nAAAUlSDzNMluruY2R88F2zmdNHheB+SY2/iR5B0AAAAMO4whEUcsIOvcEiCuu84zwcRTPAGhrS1Y\nco2NxYuldev8JwdVVppFv+9+132BLxw21UEOHBj8+qGQuRienEDgtagYl/hvv3evVFFhXjMalc4+\nW2pq0nOV9Wr5dli7dzu/TK4uBpekIAlAFolXWVlYzmLCz+ZJa1R5cJ+WyDvRx9r550tvvpm51ys0\nI0ea6jnRqD499J763jumkyelTyvC+rBinCKnTdS0syt0xpiB/8+D4iqeHFMsKitNMtP55w9UN3r9\ndWn37qGJPgsWSFu3erdCczjv5SxRKNX//zffNF+nOt9bjsee0GJdriccvz91qvTQQwMFxLKB8SMS\nEQ8AkF9dXdJtt5kikHGTJ5spbDbHA1mXjSot6STgBJkv5Vp7u6mq6dU6q7zcVDnO13Fi2Cv55J2n\nn366tPsHwkrJ95GEFeIAxAAk4gCGWxywwIo45hOQLD83glRUufNO07Yl1R1tkye7tzryY80a83Mt\nKoAEfv1f/MJfRRRpYNHPKUkpMQFjw4bU/4YLFgxciM5SWelt20yBjp6eDkkNg75XXW26BpG4k2MW\niVeBS7o7Jf6cWsju6O5OioIEARN+vnfhJp3/aptV5R1r48aZqi4YUF5uYiX++3n//cCVgzqUfDYo\nEqlaoSV/f+RIadIkk/yTKhFo1izp0kult98e3ArNz7a/+pX0xz9Khw97Xzzxya3yTtyqVdIDD6T/\ns5zGB8wlkIh4gMQ6FAziIPe2bjW5zT09Q7+Xk7lcinWCjvPPV8Pf//3QuYqfZJyg1XG8pJOAE6RS\naa5FIiYJ3qsl1vTpJkk+iwntnA8gBZtPVGT5mAAAAAAAKB5+KgVs3GgWtiTzfOL37r3XXDw9cWLo\nz4hGM9tfPX6c2VBZaRYUw+GB2xa9KgUlLiiGw/a93ZcuTX3nm9PzGbJokfTww9L3vz/4GnNJ3K1Z\nrMLh9H/vtnGX+DPr6lK3rUuO6bj6evO8W1JbVZXevaBer7wqfV1U9gSYAAAgAElEQVQbNULO/3dO\nKKRyWV4AHj+e5J1kJ06YP59+mu8jyR+vBIJYTPrkExOzV1yRevsDB6Snnhr83N1322+brSpwkk6o\nTG1q8twu8c57AABQmrZtk775zdSJO5J5fu3aLFZRdUqwKS83yc6JCTZO227cODQZJxIx26aaY0Sj\nA89HIv6TT9ravKvpRqNmu+S5mO0aRibXOvwKh6VLLnG/USoclj73ueHTshZFpyQq7xT5WwAAAECO\nMYZEHLEwjPlJ0il0a9aYhAS/ba1sJFcaSf53mzJFmjFD2rNncNWFXLVlATLNb3l6i7tiu/5Hq/52\neURbDszWOep2/NEHNVFn6ogqZVHm/ZZbpB//uLjOVUCaDuosnaO3dEzuny+XXSZ1dGTvOBg/IhHx\nAAC519UlrVgxOHFntCKqV6ea1KZp2qd9mqY2Nemtqnr952/Dmb0pw0/7Kclfq6pstqdassSuYu/i\nxdITSW1Ki6HyTlwkYo4/VZvZJUtYq0DeUXknF7LRexAAAAAAkL7k+dqePamraxSjeGUcmwog4bA0\nYYJdcpJTpZFMVEQBCpnfGLeo8FMnaUNrWG98pU6hj6Qq9Q6qwNOnSvWqSm+eVqvLxryoync97lat\nrjYLz3fd5a+VHVDEoirXVi3yTNwBAACl77bbBifuPKhm1alL1Unj7K9ro3p6q/TuyjppX4A2U046\nO03yvpveXrNd/GubbZcuTa86jhfbir2ptmtqMpWCvJKKmryrJGZdOCwtX27+AEWmLN8HkAnz53eo\nocH0NezqysMBNDebrMnGRrNw8uST5u/GRvN8c3MeDmr46cjmLTUoGsQBiAFIxAEM4gC2iJUSEomY\nu9RaWszdVC0t5uL5rFmD52vd3dK77/YvOnXk9aDTVFU1cNNIXZ25Yy/eyiuustI8v2yZ6eu+aZO5\nG27xYvP3b39r/iQ+t2mTufOvNYMLnAWOcwGkgHEQT/hZv97c4bl+vXmckPi2aJH02cdbtWTKLjVq\nk9ZrjZ7QYq3XGjVqk5ZM2aXT2h/WqPpL3G8Ci5d5Hz/e/f/8tGnS6NH+3wskFfnnQrELhYY8fm/E\nRP1OjfqaHrJ6icmTM3MofC4AsMX5AhJx4CnVfL293bm9kYvEFpmjFVGdunSOuoe0qB2hqM5Rt2oO\ndQX6OY5cEmw64l/EE2z8JONI2W1P1dQ0dO6QzCkBJ37DkJv4+gQ4H0BSsDgoico7L7888PXWrdKD\nD2apf2Eq2ew9CAAAACC3qKhZHBJ/T3v3ShUVZmL4wQdSX5900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9bQuGpqelkRGlX3zRX5/n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y+zYoVN6ixAHABwxf4CEnEAQxxAIg5gksTBsvQ3o8bl81br3m8G9+zswv35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\n04m8VtWNIhwmbceO0llnvMKd0RYsKPfkGaHFVGp2/jiYnXc60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" ], "metadata": {}, "output_type": "display_data", "text": [ "/Users/andrei/Documents/zPost_old/zPost/ipynb/Hyper_Flux/2nd_High_Lift_Prediction_Workshop/1_11.png" ] } ], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Convergence" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from zutil.post import residual_plot, get_case_report\n", "residual_plot(get_case_report(case_name))\n", "show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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SVpY/9YQA49VEJiYy8UYuJjIxkUlw0nfST926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55Zfn6quvzvnnn58kufjii3PzzTfnK1/5yuu+70ajkY9+9KNL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+jgwYPKy8tTXl5ecjX8SGWbfj78UNq+3b1aSisokKpFfPnOEqHZh2PVHrmYyMREJvbIxUQm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\n0vr1Ziqu4483v/h9+mlT/DN4sLntrLOkCROk//f/zDD8o0YpeMMNZ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"text": [ "" ] } ], "prompt_number": 21 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cleaning up" ] }, { "cell_type": "code", "collapsed": false, "input": [ "if remote_data:\n", " print 'Disconnecting from remote paraview server connection'\n", " Disconnect()\n", " pass" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Disconnecting from remote paraview server connection\n" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 } ], "metadata": {} } ] }