Howtos for jpsreport

[chraibi]

The documentation of jpsreport comes with two different Howtos:

1. How to calculate the fundamental diagram
2. How to produce density, velocity and flow profiles.

Data from the db-archiv should be used.

These Howtos are meant to explain the steps to follow, in order to produce the abovementioned results from experimental (or simulation) data.

This issue can only be solved after this PR #626

[chraibi]

@gjaeger are you working on this issue?

[gjaeger]

@chraibi yes

[gjaeger]

As quick workaround, a few python functions for Method D an J:

requirements

import re

import numpy as np
import pandas as pd

import matplotlib.pyplot as plt
import matplotlib.cm as cm
from matplotlib.patches import Polygon as ppolygon
from mpl_toolkits.axes_grid1 import make_axes_locatable

function to read IFD files

def read_IFD(IFD_filename):

    file_IFD = '{0}.dat'.format(IFD_filename)

    df = pd.read_csv('{0}'.format(file_IFD),
                     comment='#',sep='\t',
                     names=['Frame','PersID','x/m','y/m','z/m','rho','vel','Voronoi_Polygon'],
                     index_col=False)
    return df

function to convert jpsreport polygon into <np.array>

def str_to_array(p):
    """
    convert jpsreport polygon into <np.array>
    --> can be converted to <Polygon.Polygon>
    """

    if not isinstance(p, str):
        raise TypeError('str_to_Array argument must be str')

    pat = re.compile(r'''(-*\d+\.?\d*, -*\d+\.?\d*),*''')
    matches = pat.findall(p)
    lst = []
    if matches:
        lst = [tuple(map(float, m.split(","))) for m in matches]
    else:
        print("WARNING: could not convert str to list")

    return np.array(lst)

function to plot IFD Voronoi polygon with density

def IFD_plot_polygon_rho(dataframe,frame_min, frame_max, xmin, xmax, ymin,ymax, fps, v_min, v_max):

    for f in range(frame_min, frame_max):

        fig = plt.figure()
        ax1 = fig.add_subplot(111, aspect='equal')

        ax1.set_title('frame {0:6d} - time {1:7.2f} s'.format(f, f/fps))

        ax1.set_xlim(xmin,xmax)
        ax1.set_xlabel(r'x / $m$')

        ax1.set_ylim(ymin,ymax)
        ax1.set_ylabel(r'y / $m$')

        divider = make_axes_locatable(ax1)
        cax1 = divider.append_axes("right", size="10%", pad="5%")

        sm = cm.ScalarMappable(cmap = cm.get_cmap('rainbow'))

        sm.set_clim(vmin=v_min, vmax=v_max)

        for i in dataframe[dataframe['Frame'] == f]['PersID'].iteritems():

                # workflow "agents voronoi polygon" in one line
                # 1. str_to_array
                # 2. adjusting the unit
                Polygon_Agent = str_to_array(dataframe['Voronoi_Polygon'][i[0]].strip())/10000

                # density value for display with colorbar
                rho = dataframe[dataframe['Frame'] == f]['rho'][i[0]]

                sm.set_array(rho)
                sm.autoscale_None()

                ax1.add_patch(ppolygon(Polygon_Agent,
                                       fc= sm.to_rgba(rho),
                                       ec='white',
                                       lw=1))

        label = r"$\rho$ /$P m^{-2}$"

        cbar = fig.colorbar(sm, cax = cax1, label = label)

        plt.show()
        plt.close()

[gjaeger]

This can also be used to work on issues #633 and #707. Instead of the density, the individual speed can also be output.