myprojects-hg-reborn

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I corrected a typo in 3D_R_plotting.R (I was misplacing a factor when
calculating the total mass).

Now lognormal_binning.R evaluates numerically the total volume in the
fitting of the final state taking into account the fact that the primary
size is non-zero and so I cannot really use the analytics (which require
an integration starting from zero).
However, the correction is actually rather small (~1%).

I fixed a bug in 3D_R_plotting.R: since V_list is already the volume of
solid in each bin, it has to be multiplied by the primary (NOT the
effective) density to provide the mass in each bin.

now 3D_R_plotting.R plots also the mass distribution.

now lognormal_binning.R also saves the effective density centered in
each bin.

A minor modification: there is no header in the experimental data files,
so I have to read them with "header=FALSE", otherwise I leave out one of
them!

Now the code 3D_R_plotting.R calculates the distance (in a least-square
sense) between the fitting of the final state and the numerical result.

Now lognormal_binning.R saves the fitting of dN/dlogd [in units of
cm^-3] of the final state in a specific file. This is later read and
used for comparisons and estimates of the distance between the fitting
of the final state and the result of the simulation.
state

I added a derivated class (using inheritance) to the code object2.py.

I added object2.py which is a very simple code calculating the area of a
rectangle using classes and instantiation.

I simply set to 1300 the particle density in smoluchowski2.py.

Now the code 3D_R_plotting.R has some extra functionalities and the
legend is positioned automatically.

I added an extra line to simulations_script to call an extra .R code for
data post-processing.

Now the code lognormal_binning.R also plots the affective density as a
function of the aggregate size.

I added some extra features to 3D_R_plotting.R (which is now geared at
the graphics one can obtain by post-processing the results of
smoluchowski2.py simulations).

I corrected a bug in the 3D plot generated by
read_results_smoluchowsky_single_simulation.R.

I changed the name of the directory where the results are saved in code
smoluchowski2.py and I added a few lines which calculate the time-scales
of the problem.

I corrected a typo in create_dir.py.

I modified lognormal_binning.R. Now the code saves the average volume
and arithmetic diameter of the particle which are both used to calculate
the time-scales in the smoluchowski2.py code.

I added the code 3D_R_plotting.R which correctly uses the lattice
package for 3D plots in R and properly save the figure in a file.

I modified the aspect ratio of the 3D plot in the code
read_results_smoluchowsky_single_simulation.R.

I modified the code read_results_smoluchowsky_single_simulation.R. It
now plots and prints correctly the 3D evolution of the number-size
distribution. One needs to use a set of specific commands to save the
figure and a has to use a command sort(decreasing=FALSE) on the time
vector when constructing the space-time grid.
constructing the

I added the code pylab_tex.py which shows some of the pylab's
capabilities to use Tex in figures.

I added the file 3D_gnuplot.plt which can be used to read a matrix of
data and plot them. However, this is not done yet [I do not know yet
how to impose my own labels on the x and y axis and how to choose the
kind of font and so on].

I added the file pylab_3D.py which gives an example of pylab
possibilities with 3D plots.

I removed the first plot from the code plot_section.py and now the plot
of the scalar (namely the axial velocity) along a cross section is fine.

I made some of the plotting options (e.g. the kind of colormap) a bit
more explicit in plot_section.py.

I added to plot plot_section.py the capability to make bilinear
interpolations to smooth the contour plot.

I added the code plot_section.py, which is useful to plot e.g. a
countour plot of the axial velocity along a cross section of the tube.
As an input, it simply needs a snapshot of v_z along the whole pipe (or
directly along a cross-section of the pipe).

Now main_output has an extra entry for a new correction in case particle
losses are found to be significant.