I used a simple program that I wrote yesterday, along with the built-in data analytics available on Matlab, to generate a CSV file containing the following information outlined in the table headers:
"Raw Vote Total"
"Modified Trump Ratio"
"Modified Biden Ratio"
"Raw Trump Votes"
"Raw Biden Votes"
"Trump Time Interval Vote Difference"
"Biden Time Interval Vote Difference"
"Total Lost Trump Votes"
"Total Lost Biden Votes"
"Trump Loses Minus Biden Loses"
A version of the program I wrote is available in this post:
https://thedonald.win/p/11Q8XThhwf/updates-to-revise-the-raw-data-v/
All it does is it takes the truncated ratios provided in the AP provided Edison data and finds the best-fit ratios that provide the closest integer value for the individual raw partisan vote totals.
After my analysis of the PA data that was provided in the post, which is of course outdated now given the time-difference to today, I found that there were SUBSTANTIAL vote loses that occurred almost exclusively on the Trump side of the data that the current lack of good precision on ratios CANNOT ACCOUNT FOR.
My methodology greatly increases the accuracy of the derived vote total count, proportional to the magnitude size of the raw voter total; essentially this means that the larger the total number of votes is at the time generally the more accurate the method gets at deriving the smaller changes.
The total number of votes that were lost EXCLUSIVELY BY TRUMP in PA was 912,357 compared to the exclusively Biden loses of 560,111!
This amounts to a total number of lost votes of 1,472,468!
There is also CLEARLY evidence of vote transfers in the data, which can be seen in the CSV file I generated, that can be opened in any spreadsheet editor software, and is available at the following download link:
I will be generating a more up to date version of this later on tonight or tomorrow (11:45 EST 11/12/2020) and I will be providing an executable and python version of the original Matlab scripts that I wrote.
I will also be generating a CSV or JSON file for all of the states containing the best-fit ratio modifications to the original data for those states provided by the AP realtime feed of the Edison data.
Again, if these ratios modifications are proven correct by the REAL original raw data of the voter timeseries Edison provides then this is a confirmed result in at least 1 state so far in the country, PA.
Here is the quick and dirty Matlab script that I wrote to revise the Edison data in the json file that NYT and AP use on their websites that attempts to find the most likely pre-roundoff forms of the 3-digit ratios that they provide. I am currently working on translating it to other programming languages and including an input reader/writer function that acts directly on the json file itself, I just have been a little busy over the past few days to spend enough time on getting this totally together.
Here it is, including the example inputs for a1, b1, and c1 respectively, and I know it looks very crude.
% Trump's 3-digit percentage, reduced from a larger 'n'-digit percentage
a1 = 0.567;
% Biden's 3-digit percentage, reduced from a larger 'n'-digit percentage
b1 = 0.414;
% The total raw number of votes
c1 = 2915801;
% A simple calculation that generates the 'm' number of significant figures
% for the raw data
dig = floor(log10(c1));
% The simple calculation that produces the number of partitions needed to
% pin-point the right ratios with the same significant figures of the raw
% data
d1 = 10^(dig+1);
% The following is the initialization of the 3 arrays that will be used to
% find the largest and smallest fractional parts of the calculation between
% the 3-digit ratios and the raw data. The goal is to find the most logical
% integer values for raw partisan vote totals that are associated with the
% input value of percentages expanded to 'm' significant figures.
u1 = ones(d1,2);
v1 = ones(d1,2);
w1 = ones(d1,2);
% The following reproduces the floating point repeated number trailing both
% the largest and smallest rounding 'm'-digit number when reduced to a
% '3'-digit number, which is just a 'm-3'-digit array of 4's
g1 = 4;
for j1 = 1:(dig-2)
g1 = g1+4*10^j1;
end
% The following produces the 'd1'-sized arrays that generates the unique
% spans of 'm'-digit ratios that when rounded to '3'-digits reproduces the
% original input ratios; 'u1' displays the 2 respective ratios; 'v1'
% displays the 2 respective artificial partisan vote totals with trailing
% decimals; 'w1' displays the 2 fractional parts of the artificial partisan
% vote totals
for j1 = 1:d1
u1(j1,1) = a1+((j1+g1)/(d1*10^3));
u1(j1,2) = b1+((j1+g1)/(d1*10^3));
v1(j1,1) = u1(j1,1)*c1;
v1(j1,2) = u1(j1,2)*c1;
w1(j1,1) = v1(j1,1)-floor(v1(j1,1));
w1(j1,2) = v1(j1,2)-floor(v1(j1,2));
end
% The following sorts a new array formed between the individual legs of
% this simple procedure, on either side of the partisan line, which yields
% the artificial partisan vote totals with the smallest number of 'm-3'
% digits for the artificial partisan vote totals, which represents the most
% likely integers-ratio pairs that relates to the original round-off errors
u2 = sortrows([w1(:,1) u1(:,1) v1(:,1)],1);
v2 = sortrows([w1(:,2) u1(:,2) v1(:,2)],1);
% The following sorts a new array formed between the individual legs of
% this simple procedure, on either side of the partisan line, which yields
% the artificial partisan vote totals with the largest number of 'm-3'
% digits for the artificial partisan vote totals, which represents the most
% likely integers-ratio pairs that relates to the original round-off errors
u3 = sortrows(u2,1,'ascend');
v3 = sortrows(v2,1,'ascend');
% The most likely raw partisan vote totals from smallest fractional
% calculation
x1 = [u2(1,2) v2(1,2) (1-u2(1,2)-v2(1,2))];
y1 = [u2(1,3) v2(1,3) (c1-u2(1,3)-v2(1,3))];
% The most likely raw partisan vote totals from largest fractional
% calculation
x2 = [u3(1,2) v3(1,2) (1-u3(1,2)-v3(1,2))];
y2 = [u3(1,3) v3(1,3) (c1-u3(1,3)-v3(1,3))];
% The two answers should point to the same result after all values are
% compared
ans1 = [x1 y1];
ans2 = [x2 y2];
% eof
If you know Matlab, this could be very useful in generating the graphs and plots needed in order to demonstrate a comfortable conclusion about the original unmodified ratios.
I will update this posts pending future developments over the next few days.
To achieve anything less than the goal of proving the legitimacy in the re-election of President Donald J. Trump would be tantamount to rewarding the bank robbers with the money they stole after having caught them in the act just before they reach their getaway car.
