# NEWS PRESENCE OF ASTEROID NAMES RELATES TO ANGLES TO SUN

NEWS PRESENCE OF ASTEROID NAMES RELATES TO ANGLES TO SUN

-- COLLECTION STARTED ON FEB 15, 2022-- EVALUATED DAILY UNTIL CIRCA FEB 15, 2027 -- HERE ARE DAILY AGGREGATE RESULTS

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(*Today'sDateandTimeofCollection&ReportingUTC*)Style[DateString[TimeZoneConvert[DateObject[Date[]],"UTC"]],"Subtitle"]

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Tue 27 Sep 2022 13:47:37

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SetDirectory["/home/rko/Documents/Wolfram Desktop/Asteroids1211"];

(*useyourowndirectoryhere*)In[]:=

sundata=Import["MinorPlanetSunData.csv"];

## Normalize and then average the daily article count across all individual minor planets

Normalize and then average the daily article count across all individual minor planets

#### Extract Dates from Imported Data

Extract Dates from Imported Data

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dates=Table[DateObject[ToString[sundata[[2;;All,1]][[i]]]],{i,1,Length[sundata[[2;;All,1]]]}]//Quiet;

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dates

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#### Check whether dates are sampled regularly day by day and check number of dates

Check whether dates are sampled regularly day by day and check number of dates

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RegularlySampledQ[dates]

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True

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Length[dates]

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224

#### Extract Article Counts and Angles of Namesakes to Sun in Radians

Extract Article Counts and Angles of Namesakes to Sun in Radians

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toanalyse=sundata[[2;;All,2;;All]];

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toanalyse[[All,-1]]

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{150.7,149.48,148.27,147.06,145.86,144.67,143.48,142.3,141.13,139.96,138.8,137.65,136.51,135.37,134.24,133.12,132.01,130.9,129.8,128.71,127.63,126.55,125.49,124.43,123.38,122.33,121.29,120.31,119.29,118.27,117.27,116.27,115.28,114.29,113.31,112.34,111.38,110.42,109.47,108.53,107.59,106.66,105.73,104.82,103.91,103.,102.1,101.21,100.33,99.45,98.57,97.7,96.84,95.99,95.13,94.29,93.45,92.617,91.79,90.96,90.15,89.33,88.52,87.72,86.92,86.13,85.34,84.55,83.77,83.,82.23,81.46,80.7,79.94,79.18,78.43,77.69,76.94,76.2,75.47,74.74,74.01,73.29,72.57,71.85,71.14,70.43,69.72,69.02,68.32,67.62,66.93,66.24,65.55,64.87,64.18,63.5,62.83,62.15,61.48,60.82,60.15,59.49,58.82,58.17,57.51,56.86,56.21,55.56,54.91,54.27,53.62,52.98,52.35,51.71,51.08,50.44,49.81,49.19,48.56,47.94,47.32,46.69,46.08,45.46,44.84,44.23,43.61,43.,42.39,41.78,41.18,40.57,39.97,39.36,38.76,38.16,37.56,36.97,36.37,35.77,35.18,34.59,34.,33.4,32.81,32.23,31.64,31.05,30.47,29.88,29.3,28.71,28.13,27.55,26.97,26.39,25.81,25.23,24.65,24.07,23.5,22.92,22.34,21.77,21.19,20.62,20.05,19.47,18.9,18.33,17.75,17.18,16.61,16.04,15.47,14.9,14.33,13.76,13.19,12.62,12.05,11.49,10.92,10.35,9.78,9.21,8.64,8.08,7.51,6.94,6.37,5.8,5.23,4.66,4.1,3.53,2.96,2.39,1.82,1.25,0.68,0.11,359.54,358.98,358.41,357.84,357.27,356.7,356.12,355.55,354.98,354.41,353.84,353.26,352.69,352.12,351.54,350.97,350.39,349.82,349.24,348.67,348.09}

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MatrixPlot[toanalyse]

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articles=Table[toanalyse[[All,i]],{i,1,Length[toanalyse[[1]]]-1,2}];

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anglesrads=Table[UnitConvert[Quantity[toanalyse[[All,i]],"AngularDegrees"],"Radians"],{i,2,Length[toanalyse[[1]]],2}];

#### For Each Minor Planet Studied, Construct Time Series for the Normalized Article Counts

For Each Minor Planet Studied, Construct Time Series for the Normalized Article Counts

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articlesTS=Table[TimeSeries[articles[[i]],{dates}],{i,1,Length[articles]}];

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normarticlesTS=Table[Normalize[articlesTS[[i]]],{i,1,Length[articlesTS]}];

#### Look at an Example, the Second One, for a Time Series of the Number of Articles Per Day

Look at an Example, the Second One, for a Time Series of the Number of Articles Per Day

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normarticlesTS[[2]]

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TimeSeries

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ListPlot[%]

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#### Similarly, Construct a Time Series for the Rounded Angular Degrees to the Sun for Each Minor Planet

Similarly, Construct a Time Series for the Rounded Angular Degrees to the Sun for Each Minor Planet

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anglesTS=Table[TimeSeries[UnitConvert[anglesrads[[i]],"AngularDegrees"],{dates}],{i,1,Length[anglesrads]}];

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justangles=Flatten[Table[Mod[Round[QuantityMagnitude[Values[anglesTS[[i]]]]],360],{i,1,Length[anglesTS]}]];

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justvalues=Flatten[Table[Values[normarticlesTS[[i]]],{i,Length[normarticlesTS]}]];

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Histogram[DeleteCases[justvalues,0],Automatic,"ProbabilityDensity"]

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#### Now, combine angles and article counts.

Now, combine angles and article counts.

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just=Transpose[{justangles,justvalues}];

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colors=,,,,,,,,,,,;

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Length[just](*thisisthenumberofdatapointstodate*)

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271264

#### Do some basic descriptive statistics. Some will be used for calculating sampling errors.

Do some basic descriptive statistics. Some will be used for calculating sampling errors.

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Length[dates]*Length[articles](*confirmationofnumberofdatapoints!*)

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271264

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tsNA4Lengths=KeySort[GroupBy[just,First->Last,Length]];

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tsNA4SD=KeySort[GroupBy[just,First->Last,StandardDeviation]];

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samplingerrors=1.95*N[Values[tsNA4SD]]/Values[tsNA4Lengths];

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Mean[samplingerrors]

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0.00014691

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N[Mean[justvalues]]

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0.0335168

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Mean[samplingerrors]/N[Mean[justvalues]]

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0.00438317

#### Calculate the average normalized article count per rounded degree.

Calculate the average normalized article count per rounded degree.

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valgrpbydeg=KeySort[GroupBy[N[just],First->Last,Mean]]

#### A periodogram is useful for seeing periodicities in the data.

A periodogram is useful for seeing periodicities in the data.

#### Compare to a random simulation to see some idea of unusual behavior.

Compare to a random simulation to see some idea of unusual behavior.

#### There is a peak at 2 and 3. Let’s get more particular about pulling out one Fourier Transform peak at 30. It corresponds to a frequency of ~12, i.e. this may be same basis as for the astrology signs. Also, there is an even stronger signal at 93, corresponding to a frequency of ~4. This may be the basis for squares.

There is a peak at 2 and 3. Let’s get more particular about pulling out one Fourier Transform peak at 30. It corresponds to a frequency of ~12, i.e. this may be same basis as for the astrology signs. Also, there is an even stronger signal at 93, corresponding to a frequency of ~4. This may be the basis for squares.

#### A spectrogram visually confirms this frequency and period. A random data spectrogram follows for comparison.

A spectrogram visually confirms this frequency and period. A random data spectrogram follows for comparison.

#### We can see more particularly the spikes directly in the Fourier transform of the data.

We can see more particularly the spikes directly in the Fourier transform of the data.

#### Going back to the data values, let’s fit the results. They look like they fit well to a normal curve.

Going back to the data values, let’s fit the results. They look like they fit well to a normal curve.

#### The match to the normal curve is seen in a probability plot.

The match to the normal curve is seen in a probability plot.

#### Let’s look closer at the sampling errors of the average normalized article counts. At most they are less than 1 percent of their corresponding values.

Let’s look closer at the sampling errors of the average normalized article counts. At most they are less than 1 percent of their corresponding values.

#### A plot of the values with error bars

A plot of the values with error bars

#### Let’s look at the peaks and the base of the peaks.

Let’s look at the peaks and the base of the peaks.

#### The base as a moving average with a window of 7 degrees may show 12 peaks, consistent with the Fourier transform peak above at 30 and perhaps substantiating the idea of (unequal) houses.

The base as a moving average with a window of 7 degrees may show 12 peaks, consistent with the Fourier transform peak above at 30 and perhaps substantiating the idea of (unequal) houses.

#### Peaks of peaks may land in house 1, 4, 7, 10; i.e. the square houses a.k.a. kendras.

Peaks of peaks may land in house 1, 4, 7, 10; i.e. the square houses a.k.a. kendras.

#### Idealized Theoretical Result

Idealized Theoretical Result

#### Visualize Angles

Visualize Angles

#### Play with sliders.

Play with sliders.

#### More documentation will be added in the coming days ... .

More documentation will be added in the coming days ... .