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testPlot.html
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testPlot.html
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<html>
<head>
<script defer src="https://pyscript.net/alpha/pyscript.js"></script>
<meta charset="utf-8">
<style>
.dmsLegend{
color:rgb(0, 0, 0);
background-color: #ffffff;
}
</style>
</head>
<body>
<py-env>
- numpy
- matplotlib
- paths:
- ./gemNumFuncs.py
- ./ndigitsets3.py
- ./alphabet.py
- ./getwordsfromdbs.py
- ./gematriac.db
</py-env>
<py-script output="dmsDiv">
import matplotlib.pyplot as plt
import gemNumFuncs as gnf
import ndigitsets3 as nds
import getwordsfromdbs as gwdb
from js import localStorage, console
teddy = gnf.DistanceTeddyBear(gwdb.getDeepMem())
if localStorage.getItem("teddy_pointBias"):
teddy.pointBias=float(localStorage.getItem("teddy_pointBias"))
if localStorage.getItem("teddy_distanceBias"):
teddy.distanceBias=float(localStorage.getItem("teddy_distanceBias"))
if localStorage.getItem("teddy_commonNds"):
teddy.commonNds=float(localStorage.getItem("teddy_commonNds"))
if localStorage.getItem("teddy_commonRoot"):
teddy.commonRoot=float(localStorage.getItem("teddy_commonRoot"))
if localStorage.getItem("teddy_commonRoute"):
teddy.commonRoute=float(localStorage.getItem("teddy_commonRoute"))
if localStorage.getItem("teddy_commonNumber"):
teddy.commonNumber=float(localStorage.getItem("teddy_commonNumber"))
if localStorage.getItem("teddy_sameWord"):
teddy.sameWord=float(localStorage.getItem("teddy_sameWord"))
if localStorage.getItem("teddy_closenessWeight"):
teddy.closenessWeight=float(localStorage.getItem("teddy_closenessWeight"))
if localStorage.getItem("teddy_numberDistanceWeight"):
teddy.numberDistanceWeight=float(localStorage.getItem("teddy_numberDistanceWeight"))
if localStorage.getItem("teddy_rootDistanceWeight"):
teddy.rootDistanceWeight=float(localStorage.getItem("teddy_rootDistanceWeight"))
if localStorage.getItem("teddy_ndsDistanceWeight"):
teddy.ndsDistanceWeight=float(localStorage.getItem("teddy_ndsDistanceWeight"))
if localStorage.getItem("teddy_maxDistance"):
teddy.maxDistance=float(localStorage.getItem("teddy_maxDistance"))
searchResults = teddy.getRestrictedWordSet_wdxy(localStorage.getItem("dataForPlot_SW"), localStorage.getItem("cipher"))
searchResults = sorted(searchResults, key = lambda x: x[2], reverse=True)
gvArray=[]
xArray=[]
yArray=[]
uniqueNumbers=[]
for i in searchResults:
if not i[1] in uniqueNumbers:
uniqueNumbers+=[i[1]]
gvArray+=[i[1]]
xArray+=[i[3]]
yArray+=[i[4]]
searchedGemVal = gnf.getGematria(localStorage.getItem("dataForPlot_SW"), localStorage.getItem("cipher"))
rootNumber = gnf.getRootNumber(searchedGemVal)
routeToRoot = gnf.getParentList(searchedGemVal)[1:]
colors=[]
for i in range(len(gvArray)):
r = 0.0
g = 0.0
b = 0.0
if len(str(gvArray[i])) == len(str(searchedGemVal)):
r = 0.0
g = 0.0
b = 0.8
if gnf.getRootNumber(gvArray[i]) == rootNumber:
r = 0.0
g = 0.8
b = 0.8
if gnf.getParentList(gvArray[i])[1:] == routeToRoot:
r = 0.8
g = 0.0
b = 0.5
if gvArray[i] == searchedGemVal:
r = 0.6
g = 0.6
b = 0.0
colors+=[[r,g,b]]
fig, ax = plt.subplots()
ax.scatter(xArray, yArray, s=6, c=colors)
fig
</py-script>
<div id="dmsDiv"></div>
<div class="dmsLegend" id = "dmsLegendDiv">
<h1>About the DMS plot</h1>
<p>
<ul>
<li style="color:rgb(0,0,0)">Black: no calculated relation</li>
<li style="color:rgb(0,0,204)">Blue: common n-digit set</li>
<li style="color:rgb(0,204,204)">Cyan: common root</li>
<li style="color:rgb(204,0,127)">Violet: common route</li>
<li style="color:rgb(153,153,0)">Camouflage green: common number</li>
</ul>
</p>
<p>
For the plot, gematria values of words
in the database are analyzed numerologically:
in addition to the gematria value,
the n-digit set,
the digital root
and the route to the root of the numbers are calculated.
</p>
<p>
The denominator of the distance measure is obtained by
giving points to a pair of numbers according to these properties.
The algorithm also calculates simple distance measures
(abs(x0-x1)) of these properties, giving the numerator.
</p>
<p>
The distance measure is calculated between
the gematria value of the search input word
and each gematria value of a word in the database.
</p>
<p>
In the plot, the gematria values are
plotted as points at their distance
from the origin with an angle relative
to the gematria value of a word in the database.
360 degrees represents the largest value in a search.
</p>
</div>
</body>
</html>