[Video/Article] Are Conspiracy Theories Inherently Irrational?

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The belief common among intellectuals that “conspiracy theories” are inherently irrational or so unlikely as to be essentially inherently irrational is demonstrably false.

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[Article] Are Conspiracy Theories Inherently Irrational?

Are Conspiracy Theories Inherently Irrational?

The belief common among intellectuals that “conspiracy theories” are inherently irrational or so unlikely as to be essentially inherently irrational is demonstrably false.

Merriam Webster defines conspiracy as “the act of conspiring together” and conspiring as “to join in a secret agreement to do an unlawful or wrongful act or an act which becomes unlawful as a result of the secret agreement.” The dictionary gives a range of word meanings for “theory” including “a hypothesis assumed for the sake of argument or investigation” which comes closest to the way “theory” is used in the phrase “conspiracy theory.”

The phrase “conspiracy theory” is used most often to mean “totally crazy conspiracy theory” or “conspiracy theory highly contradicted by the evidence” instead of the dictionary meaning of “conspiracy theory.” The phrase is often paired with “conspiracy theorist” and assertions that the conspiracy theory has been “debunked.” It is sometimes paired with the phrases “denier,” “denialism,” and “pseudoscience.” For example, “COVID denier” or “AIDS denier.”

“Conspiracy theory” is usually invoked to attack theories, evidence of misconduct, evidence of conflicts of interests, and even evidence of simple mistakes by authority figures in the group or groups — ethnic, professional, political, social — that the author identifies with or belongs to. In some case the author or authors using the phrase “conspiracy theory” are the subjects of the suspicions or evidence: https://thebulletin.org/2021/08/how-covid-19s-origins-were-obscured-by-the-east-and-the-west/.

“Conspiracy theory” is often invoked in discussions of murders or possible murders (suspicious “suicides” such as Jeffrey Epstein, accidents etc.) such as the assassination of President Kennedy or mass killings with actual or potential large scale political or economic consequences such as the September 11, 2001 terrorist attacks. A high percentage of “conspiracy theories” involve assassinations, mass killings, and other murders or possible murders. These include the assassination of President Kennedy in 1963, his brother Robert Kennedy in 1968, Martin Luther King in 1968, Malcolm X in 1965, US Senator Huey Long in 1934, the mysterious crash of TWA Flight 800 in 1996, the Columbine mass school shooting in 1999, the September 11, 2001 terrorist attacks, the anthrax attacks just after the September 11 attacks in 2001 blamed on Bruce Ivins, and the COVID-19 pandemic.

“Conspiracy theory” is often paired with the explicit or implicit argument that “someone would have talked” and that an actual conspiracy could not keep its activities sufficiently secret to avoid prosecution or successfully organize a group, often implied to be large, of people covertly.

Conspiracy is recognized in common law and groups of people are convicted of conspiracy all the time. (UPDATE: August 29, 2021) See, for example, 18 US Code 371: Conspiracy to commit offense or to defraud United States

If two or more persons conspire either to commit any offense against the United States, or to defraud the United States, or any agency thereof in any manner or for any purpose, and one or more of such persons do any act to effect the object of the conspiracy, each shall be fined under this title or imprisoned not more than five years, or both.

If, however, the offense, the commission of which is the object of the conspiracy, is a misdemeanor only, the punishment for such conspiracy shall not exceed the maximum punishment provided for such misdemeanor.

Some recent conspiracy convictions found with the DuckDuckGo search engine on August 23, 2021:

https://www.justice.gov/usao-ma/pr/sutton-man-convicted-cocaine-conspiracy

https://www.justice.gov/opa/pr/man-convicted-conspiracy-import-and-distribute-fentanyl

https://qctimes.com/news/local/crime-and-courts/two-iowa-men-convicted-for-conspiracy-to-distribute-meth-in-rock-island/article_ff3a06e5-1624-5016-a342-b59d7618aff4.html

https://www.newsbreak.com/news/2333127480906/bronx-man-convicted-of-murder-for-hire-conspiracy-drug-trafficking-and-firearms-offenses

https://www.indystar.com/story/news/local/hamilton-county/2021/08/05/white-collar-crime-banc-serv-defendants-found-guilty-wire-fraud/5504238001/

The US Federal Bureau of Investigation’s homicide statistics for 2019 attribute 14.7 percent of the total 13,927 murders that year to “multiple offenders,” meaning a conspiracy in common usage even if the case does not meet the legal definition of conspiracy or conspiracy was not charged for some other reason.

https://ucr.fbi.gov/crime-in-the-u.s/2019/crime-in-the-u.s.-2019/topic-pages/tables/expanded-homicide-data-table-4.xls

The same table attributes 27.8% of these murders to an unknown offender or offenders. This is particulary significant because a high proportion of unsolved murders in the United States are attributed to “gang violence” or “organized crime,” meaning a conspiracy in common usage.

UPDATE: August 29, 2021

See, for example:

Chen, Elsa Y. “Victim and Witness Intimidation.” Encyclopedia of Race and Crime. Editors Helen Taylor Greene and Shaun L.
Gabbidon. Thousand Oaks, CA: Sage Publications (2009)

Community-wide implicit victim and witness intimidation has become particularly severe and widespread in neighborhoods dominated by gangs and drugs, including many predominantly African American and Latina/o inner-city areas. Asian gangs also engage in intimidation. A well-known example of implicit intimidation is a grassroots campaign known as “Stop Snitchin’,” which began in Baltimore around 2004 and quickly spread to other urban areas by several means, including CDs and DVDs, websites, T -shirts, and rap lyrics. The movement’s purpose is to urge community members not to cooperate with criminal justice authorities, and to remind them that “snitches wear stitches.” Gang members have appeared in courtrooms wearing Stop Snitchin’ T -shirts, provoking efforts to ban the shirts by judges and political officials. The success of the Stop Snitchin’ movement can be attributed in part to some community members’ anger regarding the high rate of incarceration among minority men, frustration over criminal justice authorities’ use of informants from the community and jails or prisons to facilitate the prosecution and incarceration process, and general mistrust of law enforcement officials, who often do not provide adequate protection for those who do cooperate and sometimes reward unreliable informants with leniency in prosecution or sentencing. The Stop Snitchin’ mantra is redolent of the Italian Mafia’s centuries- old code known as omerta, an oath of silence prohibiting cooperation with the authorities under any circumstances.

The culture and attitudes surrounding rap and hip hop music have perpetuated the practice of victim and witness intimidation and the implicit code of silence . Some well-known rap artists have refused to cooperate in criminal cases in which they or their friends or members of their entourages were victims or suspects. Other popular rappers have produced songs urging listeners not to speak with the police. The murders of several rap and hip hop stars, including Tupac Shakur, the Notorious B.I.G., and Jam Master Jay from Run D.M.C., remain unsolved because of witnesses’ unwillingness to violate the “code of the street” by speaking to the police or testifying in court.

END UPDATE

“Over 1,000 victims, 126 dead, just 2 convictions: 6 years of mass shootings in Chicago” By Tom Schuba and Andy Grimm, August 2, 201, Chicago Sun Times

https://chicago.suntimes.com/crime/2021/8/2/22559312/mass-shooting-victims-gun-violence-crime-cpd-police-department-clearance-murder-arrest

https://www.theguardian.com/us-news/2016/sep/04/chicago-gun-violence-unsolved-murders-deadly-year

Online searches for gang violence and unsolved murders often turn up articles and official reports using the phrases “gun violence” and emphasizing the race of both victims and suspected perpetrators of unsolved murders, usually in so-called “inner city” neighborhoods. Nonetheless careful reading show these articles and reports are talking about unsolved gang murders often involving drugs or other illegal activities: criminal conspiracies.

Some Famous (or Infamous) Unsolved “Organized Crime” Murders

Arnold Rothstein — November 6, 1928 — reputed prohibition era mob boss and “mentor” of Charles “Lucky” Luciano, Meyer Lanksy, and other famous mobsters.
Benjamin “Bugsy” Siegel — June 20, 1947 — close associate of Lucky Luciano and Meyer Lansky
Albert Anastasia — October 25, 1957 — alleged mob boss and head of “Murder Inc.”
“Sam” Giancana — June 19, 1975 — alleged second in command of the Chicago Mob
“Johnny” Roselli — August 9, 1976 — alleged top figure in the Chicago Mob
Jimmy Hoffa — Teamsters Union Boss, disappeared July 30, 1975, declared dead July 30, 1982
John Favara — disappeared July 28, 1980, declared dead 1983, neighbor who disappeared after a car accident that killed mob boss John Gotti’s 12 year old son Frank Gotti. Favara was the driver of the car.

The history of organized crime in the United States is replete with many more unsolved murders and disappearances. In fact, people often don’t talk — at least not under oath in a court of law — and innocent witnesses often keep silent fearing retaliation from organized crime groups — conspiracies. Circumstantial evidence, suspicions, and rumors abound as is the case with murders such as the assassination of President Kennedy where the phrase “conspiracy theory” is invoked to ridicule and stigmatize those same sort of circumstantial evidence, suspicions, and rumors.

The empirical data from murder statistics and the history of murders is that criminal conspiracies are common — at least fourteen (14) percent of murders — and often escape successful prosecution. A high fraction of events where the phrase “conspiracy theory” is used to ridicule and stigmatize suspicions or even evidence of a conspiracy are also murders or possible murders: political assassinations, terrorists incidents, mass killings, suspicious accidents and suicides.

Are there high level international conspiracies?

High level international criminal conspiracies involving seemingly independent and even competing groups such as companies have been demonstrated in court in a number of cases.

Archer Daniels Midland (ADM), Pfizer (yes the COVID vaccine company), and several other companies in Japan and Korea engaged in an illegal criminal conspiracy to fix the price of citric acid — a key ingredient of Coca-Cola and other sodas — and other food additives such as lysine in the 1990’s, probably beginning in the 1980’s or earlier. At this time, ADM and Pfizer dominated world production and sales of citric acid, an important and lucrative product due to its key rol in Coca-Cola and other sodas.

See for example:

https://www.justice.gov/archive/atr/public/press_releases/1996/1030.htm

https://www.researchgate.net/publication/227645450_The_global_citric_acid_conspiracy_Legal-economic_lessons

https://link.springer.com/chapter/10.1007/3-540-34222-2_5

“Archer Executive Testifies to Fixing Citric Acid Prices” by Kurt Eichenwald, New York Times, August 13, 1998

“The Informant: A True Story” by Kurt Eichenwald, Crown, October 15, 2001

Rats in the Grain: The Dirty Tricks and Trials of Archer Daniels Midland, the Supermarket to the World, by James B. Lieber, Basic Books, January 10, 2002

Antitrust investigations have demonstrated several cases of international price fixing cartels for various products. Some price fixing arrangements are open agreements such as OPEC, the Organization of Petroleum Exporting Countries, and the proration of oil production in Texas by the Texas Railroad Commission. https://rrc.texas.gov/about-us/rrc-history/

Governments can and do provide cover, sanction, and legitimacy for price fixing arrangements that would be illegal and criminal otherwise.

Do Cartels Get Away with Illegal Price Fixing Agreements?

Some would argue that because ADM, Pfizer, and the other conspirators were caught, they did not get away with the crime and this somehow proves no one is able to get away with a criminal conspiracy. This, of course, is demonstrably not true for unsolved murders. In contrast to murders, where the crime is usually clear due to a dead body and signs of violence, proving a rise in prices is a crime — is due to a criminal conspiracy is inherently difficult.

There are a number of cases of mysterious increases in the price of various products and services, circumstantial evidence of price fixing and collusion as defined in antitrust law. A notable recent example is the remarkable increase in the price of insulin, a product produced in vats using well established genetically engineered bacteria. There is no obvious shortage of a key ingredient or other explanation.

See for example:

https://www.finance.senate.gov/chairmans-news/grassley-wyden-release-insulin-investigation-uncovering-business-practices-between-drug-companies-and-pbms-that-keep-prices-high

Despite widespread plausible suspicions, proof of an insulin price fixing agreement similar to the citric acid price fixing agreement remains elusive.

Another possible example is the marked rise in oil prices following a series of mergers of major oil companies in the late 1990’s and early 2000’s.

British Petroleum Amoco Merger: (August 1998): https://money.cnn.com/1998/08/11/deals/bp/

Exxon Mobil Merger (November 1999): https://money.cnn.com/1999/11/30/deals/exxonmobil/

Conoco Phillips Merger (November 2001): https://money.cnn.com/2001/11/19/deals/phillips_conoco/

Conclusion

Conspiracy theories are rational in many cases. Historical data on unsolved murders demonstrates that proving a conspiracy beyond a reasonable doubt is difficult however. Criminal conspiracies are NOT like laboratory experiments or astronomical observations. The belief common among intellectuals that “conspiracy theories” are inherently irrational or so unlikely as to be essentially inherently irrational is demonstrably false.

(C) 2021 by John F. McGowan, Ph.D.

About Me

John F. McGowan, Ph.D. solves problems using mathematics and mathematical software, including developing gesture recognition for touch devices, video compression and speech recognition technologies. He has extensive experience developing software in C, C++, MATLAB, Python, Visual Basic and many other programming languages. He has been a Visiting Scholar at HP Labs developing computer vision algorithms and software for mobile devices. He has worked as a contractor at NASA Ames Research Center involved in the research and development of image and video processing algorithms and technology. He has published articles on the origin and evolution of life, the exploration of Mars (anticipating the discovery of methane on Mars), and cheap access to space. He has a Ph.D. in physics from the University of Illinois at Urbana-Champaign and a B.S. in physics from the California Institute of Technology (Caltech).

[Video] Afghanistan, Abiogenic Oil, and the Pipeline That Never Was

https://odysee.com/@MathematicalSoftware:5/afghanistan-abiogenic-oil-and-the-pipeline-that-never-was

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Short video on Afghanistan, the abiogenic oil hypothesis, and the remarkable failure of the US to build a natural gas pipeline from Turkmenistan through Afghanistan and Pakistan to the Indian Ocean despite spending at least one trillion dollars and twenty (20) years of military occupation of the country — a pipeline that would be a win win proposition for just about everyone! Incompetence or a hidden agenda to boost world energy prices?

Deep Hot Biosphere by Thomas Gold: https://www.amazon.com/Deep-Hot-Biosphere-Fossil-Fuels/dp/0387985468

Oil and natural gas on Mars: http://jmcgowan.com/oil_spie.pdf

My interview on Martian Methane, abiogenic oil, and the origin of life: http://wordpress.jmcgowan.com/wp/interview-about-martian-methane-and-life-on-event-horizon-show/

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(C) 2021 by John F. McGowan, Ph.D.

About Me

John F. McGowan, Ph.D. solves problems using mathematics and mathematical software, including developing gesture recognition for touch devices, video compression and speech recognition technologies. He has extensive experience developing software in C, C++, MATLAB, Python, Visual Basic and many other programming languages. He has been a Visiting Scholar at HP Labs developing computer vision algorithms and software for mobile devices. He has worked as a contractor at NASA Ames Research Center involved in the research and development of image and video processing algorithms and technology. He has published articles on the origin and evolution of life, the exploration of Mars (anticipating the discovery of methane on Mars), and cheap access to space. He has a Ph.D. in physics from the University of Illinois at Urbana-Champaign and a B.S. in physics from the California Institute of Technology (Caltech).

[Video] CDC Provincetown COVID-19 Outbreak Data Does NOT Show Vaccines Working

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The Provincetown, Massachusetts (Cape Cod, Barnstable County) COVID-19 outbreak does NOT prove that the COVID-19 vaccines are working as widely claimed in the mainstream media. (See the CDC Report “Outbreak of SARS-CoV-2 Infections, Including COVID-19 Vaccine Breakthrough Infections, Associated with Large Public Gatherings — Barnstable County, Massachusetts,” https://www.cdc.gov/mmwr/volumes/70/wr/pdfs/mm7031e2-H.pdf)

Article: http://wordpress.jmcgowan.com/wp/article-us-cdc-provincetown-covid-19-outbreak-data-does-not-show-vaccines-work/

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(C) 2021 by John F. McGowan, Ph.D.

About Me

John F. McGowan, Ph.D. solves problems using mathematics and mathematical software, including developing gesture recognition for touch devices, video compression and speech recognition technologies. He has extensive experience developing software in C, C++, MATLAB, Python, Visual Basic and many other programming languages. He has been a Visiting Scholar at HP Labs developing computer vision algorithms and software for mobile devices. He has worked as a contractor at NASA Ames Research Center involved in the research and development of image and video processing algorithms and technology. He has published articles on the origin and evolution of life, the exploration of Mars (anticipating the discovery of methane on Mars), and cheap access to space. He has a Ph.D. in physics from the University of Illinois at Urbana-Champaign and a B.S. in physics from the California Institute of Technology (Caltech).

[Article] US CDC Provincetown COVID-19 Outbreak Data Does NOT Show Vaccines Work

The Provincetown, Massachusetts (Cape Cod, Barnstable County) COVID-19 outbreak does NOT prove that the COVID-19 vaccines are working as widely claimed in the mainstream media. (See the CDC Report “Outbreak of SARS-CoV-2 Infections, Including COVID-19 Vaccine Breakthrough Infections, Associated with Large Public Gatherings — Barnstable County, Massachusetts,” https://www.cdc.gov/mmwr/volumes/70/wr/pdfs/mm7031e2-H.pdf)

Media reports have often claimed that because no deaths were reported during the outbreak this proves the COVID-19 vaccines work. These media reports often make a comparison to deaths in January of 2021 when widespread vaccination began without actually citing or discussing estimates of the infection fatality rate (IFR) of COVID-19, let alone discussing the seasonality of the infection fatality rate of respiratory diseases.

Note that the infection fatality rate (IFR) of a disease is different from the case fatality rate (CFR) often reported in the mass media, sometimes as several percent for COVID-19 cases. The Case Fatality Rate is generally much larger than the infection fatality rate for many diseases and for COVID-19. Even medical experts who should know better mistakenly conflate CFR and IFR. IFR includes unreported and asymptomatic “cases” whereas CFR does not, hence the lower rate.

Both CFR and IFR can be difficult to compute, estimate, and compare because of varying definitions of “cases” and properly identifying the number of unreported cases with actual symptoms and asymptomatic infections. The COVID-19 pandemic has involved rapidly changing case definitions and poorly validated tests (PCR, antigen, antibody, and even other experimental tests).

Because the CDC was able to identify asymptomatic cases during the Provincetown outbreak, the IFR should be used to evaluate the published data, although the CDC may have missed some, possibly many asymptomatic cases.

Overall, 274 (79%) vaccinated patients with breakthrough infection were symptomatic.

CDC Report, Outbreak of SARS-CoV-2 Infections, Including COVID-19 Vaccine Breakthrough Infections, Associated with Large Public Gatherings — Barnstable County, Massachusetts, July 2021, Page 1

Similar claims that the outbreak proves the vaccines work have been made regarding the COVID-19 outbreak in heavily vaccinated Israel, so far involving a large fraction of vaccinated persons — often reported as 95% of the COVID-19 cases in Israel.

The CDC Provincetown, Massachusetts (Barnstable County, Cape Cod) data is analyzed in detail below.

According to the CDC report, 469 Massachusetts residents were identified with COVID-19 attributed to attending public events in “a town in Barnstable County, Massachusetts.” This is Provincetown on Cape Cod (Barnstable County, MA is Cape Cod). Of these, 346 (about 74 %) had been fully vaccinated with COVID-19 vaccines: both shots of the Moderna vaccine, both shots of the Pfizer vaccine, and the one shot J&J vaccine at least 14 days prior to infection with COVID-19. One-hundred and twenty-three (about 26 %) were unvaccinated. According to the CDC report, 69% of Massachusetts residents are fully vaccinated against COVID-19. Four fully vaccinated persons were hospitalized and only one unvaccinated person was hospitalized.

The ratio of vaccinated to unvaccinated infected persons (74%) is statistically indistinguishable from the 69% of vaccinated persons in Massachusetts. The most straightforward interpretation of this ratio is that the vaccine failed completely. If the vaccine substantially reduced the risk of infection, we would expect all or most of the cases to be in unvaccinated persons.

It is possible to explain away the ratio of infections by arguing that the vaccinated persons were overconfident, believing the media hype implying the vaccines confer full immunity (“safe and effective” repeated without qualification by most of the mass media). Hence they may have taken risks that spread the disease despite the vaccine, offsetting the benefits of the vaccine. It is clearly remarkable that this exactly offset the effect of the vaccines to produce the same ratio as the fraction of unvaccinated in the general Massachusetts population (the 69%).

The ratio of hospitalized vaccinated to unvaccinated persons (80%) is also statistically indistinguishable from the 69% of vaccinated persons in Massachusetts. The implied hospitalization rate of the vaccinated is 1.16% The implied hospitalization rate of the unvaccinated is only 0.81%. The hospitalization rates are statistically equivalent. Hospitalization is an imperfect proxy for severity of the disease. Consequently, the reported data do NOT show that the vaccines reduced the severity of the COVID-19 disease, contrary to claims by the US CDC, mainstream media, and others.

It is harder to explain away the hospitalizations, a proxy for serious COVID-19. The data gives statistically the same hospitalization rate for vaccinated and unvaccinated persons once infected. Again, however, one could argue the unvaccinated are healthier than the vaccinated to explain the absence of a preponderance of hospitalizations in the unvaccinated. In other words, very healthy persons are correctly less worried about handling a COVID-19 infection without the vaccine and refrain from taking the experimental, largely untested vaccine at this time.

The infection fatality rate (IFR) of COVID-19 (see — for example — https://www.who.int/bulletin/volumes/99/1/20-265892.pdf, https://pubmed.ncbi.nlm.nih.gov/33289900/) is generally estimated as only a few tenths of a percent for “young” (under 70) healthy persons — most of this in persons over 40. The Provincetown COVID-19 outbreak involved July 4th holiday party-goers, generally a young, healthy group. Using an IFR of 0.3%, we might naively expect about 1.2 deaths on average with an uncertainty of 1 death. Hence the absence of even one death in the Provincetown data is hardly surprising and does NOT show that the vaccines are working — even without considering the seasonality of respiratory diseases.

The incidence and mortality of respiratory diseases is seasonal with more infections, cases, hospitalizations, and death in January than in July. The reasons for this pattern are not understood, although it is often attributed to spread of the diseases among school-children in the United States. The sinusoidal — pendulum-like — variation shows no sign of a step up when school opens in the fall or a step down when schools close in the spring/early summer. The pattern suggests the variation is directly or indirectly driven by the Sun.

A number of causes for the seasonality of respiratory diseases have been suggested including vitamin D — which is involved in the immune system — production by sunlight, destruction of the respiratory disease causing organisms by sunlight and specifically the UV light in sunlight, unexplained benefits of sunlight other than vitamin D production, less energy diverted to staying warm when the Sun is stronger, and lower absolute humidity during the cold winter in many regions (NOT ALL) causing longer persistence in the atmosphere of aerosol particles with viruses and sometimes bacteria. In 2020, the COVID outbreak was highly seasonal in most nations with the exception of a surge in the summer in parts of the United States, possibly associated with the George Floyd/Black Lives Matter mass protests.

In conclusion, the CDC’s Provincetown COVID-19 outbreak data does NOT show the vaccines are working — to reduce infections, reduce severity of the disease, or prevent death. The statistics for serious cases of COVID-19 in both the unvaccinated and vaccinated persons are quite small — only five persons (four fully vaccinated, one unvaccinated) hospitalized and no deaths. The lack of deaths is not surprising given the low infection fatality rate (IFR) of COVID-19. Much larger statistics are needed to properly estimate any benefits or harms from the vaccines.

(C) 2021 by John F. McGowan, Ph.D.

About Me

John F. McGowan, Ph.D. solves problems using mathematics and mathematical software, including developing gesture recognition for touch devices, video compression and speech recognition technologies. He has extensive experience developing software in C, C++, MATLAB, Python, Visual Basic and many other programming languages. He has been a Visiting Scholar at HP Labs developing computer vision algorithms and software for mobile devices. He has worked as a contractor at NASA Ames Research Center involved in the research and development of image and video processing algorithms and technology. He has published articles on the origin and evolution of life, the exploration of Mars (anticipating the discovery of methane on Mars), and cheap access to space. He has a Ph.D. in physics from the University of Illinois at Urbana-Champaign and a B.S. in physics from the California Institute of Technology (Caltech).

[Video] COVID Vaccine Failure in CDC Barnstable, Massachusetts Data

COVID Vaccine Failure in CDC Barnstable, Massachusetts Data Video on Odysee

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The July 30, 2021 CDC report on an outbreak of COVID-19 (delta variant) in Barnstable County, Massachusetts (July 6-25, 2021) used to justify guidance for vaccinated persons to wear masks indoors appears to indicate the COVID vaccines failed to reduce either the rate of infection with COVID in the vaccinated or the hospitalization rate if infected, contrary to widespread statements in the mass media by public health authorities and others. Although there are some possible if improbable explanations, no explanation for this seeming contradiction appears to be present in the report.

Outbreak of SARS-CoV-2 Infections, Including COVID-19 Vaccine Breakthrough Infections, Associated with Large Public Gatherings — Barnstable County, Massachusetts, July 2021
Brown et al
Morbidity and Mortality Weekly Report
Early Release / Vol. 70 July 30, 2021
U.S. Department of Health and Human Services
Centers for Disease Control and Prevention
https://www.cdc.gov/mmwr/volumes/70/wr/pdfs/mm7031e2-H.pdf

Relevant Numbers from Report:

cases of COVID-19 associated with multiple summer events and large public gatherings in a town in Barnstable County, Massachusetts (July 6-25, 2021) 469
vaccination coverage among eligible Massachusetts residents 69.00%
cases in fully vaccinated 346 73.77%
cases in unvaccinated, which may include partially vaccinated and recently vaccinated (less than 14 days since final shot) 123 26.23%
number of vaccinated hospitalized 4
number of unvaccinated hospitalized 1
hospitalization rate of vaccinated infected with COVID-19 1.16%
hospitalization rate of unvaccinated infected with COVID-19 0.81%
Number of cases sequenced 133
Number of cases with B.1.617.2 (Delta) variant 120 90.23%
Number of symptomatic vaccinated cases 274
Number of symptomatic unvaccinated cases ?

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#

(C) 2021 by John F. McGowan, Ph.D.

About Me

John F. McGowan, Ph.D. solves problems using mathematics and mathematical software, including developing gesture recognition for touch devices, video compression and speech recognition technologies. He has extensive experience developing software in C, C++, MATLAB, Python, Visual Basic and many other programming languages. He has been a Visiting Scholar at HP Labs developing computer vision algorithms and software for mobile devices. He has worked as a contractor at NASA Ames Research Center involved in the research and development of image and video processing algorithms and technology. He has published articles on the origin and evolution of life, the exploration of Mars (anticipating the discovery of methane on Mars), and cheap access to space. He has a Ph.D. in physics from the University of Illinois at Urbana-Champaign and a B.S. in physics from the California Institute of Technology (Caltech).

[Article] Improving CDC Data Practices: Recommendations for Improving the United States Centers for Disease Control (CDC) Data Practices for Pneumonia, Influenza, and COVID-19

This is a preprint of a new academic paper written by Tam Hunt, Josh Mitteldorf, Ph.D. and myself on the US Centers for Disease Control (CDC)’s data practices during the COVID-19 pandemic and for pneumonia and influenza prior to the pandemic. I am the corresponding author.

Abstract

During the pandemic, millions of Americans have become acquainted with the CDC because its reports and the data it collects affect their day-today lives. But the methodology used and even some of the data collected by CDC remain opaque to the public and to the community of academic epidemiology. In this paper, we highlight areas in which CDC methodology might be improved and where greater transparency might lead to broad collaboration. (1) “Excess” deaths are routinely reported, but not “years of life lost”, an easily-computed datum that is important for public policy. (2) What counts as an “excess death”? The method for computing the number of excess deaths does not include error bars and we show a substantial range of estimates is possible. (3) Pneumonia and influenza death data on different CDC pages is grossly contradictory. (4) The methodology for computing influenza deaths is not described in sufficient detail that an outside analyst might pursue the source of the discrepancy. (5) Guidelines for filling out death certificates have changed during the COVID-19 pandemic, preventing the comparison of 2020-21 death profiles with any previous year. We conclude with a series of explicit recommendations for greater consistency and transparency, and ultimately to make CDC data more useful to outside epidemiologists.

John F. McGowan, Ph.D., Tam Hunt, Josh Mitteldorf. Improving CDC Data Practices Recommendations for Improving the United States Centers for Disease Control (CDC) Data Practices for Pneumonia, Influenza, and COVID-19. Authorea. July 19, 2021.
DOI: 10.22541/au.162671168.86830026/v1

Here are the key recommendations from the paper:

Recommendations

In light of the previous discussion, we make a number of recommendations to improve CDC’s data practices, including improved observance of common scientific and engineering practice – such as use of significant figures and reporting of statistical and systematic errors. Common scientific and engineering practice is designed to prevent serious errors and should be followed rigorously in a crisis such as the COVID-19 pandemic.

Note that some of these recommendations may require changes in federal or state laws, federal or state regulations, or renegotiation of contracts between the federal government and states. This is probably the case for making the Deaths Master File (DMF), with names and dates of death of persons reported as deceased to the states and federal government, freely available to the public and other government agencies.

  • All CDC numbers, where possible, should be clearly identified as estimates, adjusted counts, or raw counts, with statistical errors and systematic errors given, using consistent clear standard language in all documents. The errors should be provided as both ninety-five percent (95%) confidence level intervals and the standard deviation – at least for the statistical errors.
  • In the case of adjusted counts, the raw count should be explicitly listed immediately following the adjusted count as well as a brief description of the adjustment and a reference for the adjustment methodology. For example, if the adjusted number of deaths in the United States in 2020 is 3.4 million but the raw count of deaths was 3.3 million with 100,000 deaths added to adjust for unreported deaths of undocumented immigrants, the web pages and reports would say:

Total deaths (2020): 3.4 million (adjusted, raw count 3.3 million, unreported deaths of undocumented immigrants, adjustment methodology citation: Smith et al, MMWR Volume X, Number Y)

  • The distinction between the leading causes of death report “pneumonia and influenza” deaths, ~55,000 per year pre-pandemic, and the FluView website “pneumonia and influenza” deaths, ~188,000 per year pre-pandemic, should be clarified in the labels and legends for the graphics and prominently in the table of leading causes of death or immediately adjacent text. Statistical and systematic errors on these numbers should be provided in graphs and tables.
  • In general, where grossly different raw counts, adjusted counts, or estimates are presented in CDC documents and websites with the same name, semantically equivalent or nearly equivalent names such as “pneumonia and influenza” and “influenza and pneumonia,” clearly distinct names should be used instead, or the reasons for the gross difference in the values should be prominently listed in the graphs and tables or immediately adjacent text. It should be easy for the public, busy health professionals, policy makers and others to recognize and understand the differences.
  • CDC should provide results for different models for the same data with similar R2 values – coefficient of determination – to give the audience a quick sense of the systematic modeling errors – since there is no generally accepted methodology for estimating the 95% confidence level for the systematic modeling errors. See Figure 7 above for an example.
  • All mathematical models should be free and open source with associated data provided using commonly used free open-source scientific programming languages such as Python or R, made available on the CDC website, GitHub, and other popular sources. The models and data should be provided in a package form such that anyone with access to a standard MS Windows, Mac OS X, or Linux/Unix computer can easily download and run the analysis – similar to the package structure used by the GNU project, for example.
  • Specifically, the influenza virus deaths model should be provided to the public as code and data. The justification for the increase in the number of deaths attributed to influenza (~6,000 to ~55,000) should be presented in clear language with supporting numbers, such as the false positive and negative rates for the laboratory influenza deaths and general diagnosis of influenza in the absence of a positive lab test as well as in the code and data.
  • With respect to excess deaths tracking, include all major select causes of death, rather than just the thirteen (13) in the cause-specific excess deaths that CDC tracks, which currently account for about 2/3 of all deaths.
  • Include a Years of Lives Lost (YLL) display for COVID-19 deathsi and non-COVID-19 deaths, as well as excess deaths analysis, due to the higher granularity of YLL analysis when compared to excess deaths analysis. Explain the pros and cons of both analytical tools. Do the same for any future pandemics or health crises.
  • Adopt or develop a different algorithm or algorithms for tracking excess deaths which are mostly attributed to non-infectious causes such as heart attacks, cancer, and strokes. The Farrington/Noufaily algorithms were specifically developed as an early warning for often non-lethal infectious disease outbreaks such as salmonella. A medically-based model or models that incorporates population demographics such as the aging “baby boom” and evolving death rates broken down by age, sex, and possibly other factors where known is probably a better practice rather than simple empirical trend models such as the Noufaily algorithm.
  • Eliminate the zeroing procedure in calculating excess deaths, in which negative excess deaths in some categories are set to zero, rather than being added to the full excess deaths sum over all categories.
  • The anonymized data with causes of death as close to the actual data as possible, e.g. the actual death certificates, should be available on the CDC website in a simple accessible widely used format such as CSV (comma separated values) files. The code used to aggregate the data into summary data such as the FluView website data files should also be public.
  • The full Deaths Master File (DMF) including the actual names of the deceased persons and dates of death should be made available to the general public, independent researchers, and others. This is critical to independent verification of many numbers from the CDC, SSA, and US Census.
  • COVID-19-related deaths figures should be tracked based on year-specific age of death, rather than 10-year age ranges, as is currently the case.
  • CDC frequently changes the structure and layout of the CSV files/spreadsheets on their websites. The CDC should either (1) not do this or (2) provide easy conversion between different file formats with each new format so it is trivial for third parties to quickly adapt to the changes without writing additional code. CDC should provide a program or program in a free and open source language like R to convert between the formats.
  • The CDC and other agencies should be required to announce and solicit public comment for changes to case definitions, data collection rules, etc. for key public policy data such as the COVID-19 case definitions, death certification guidelines, and coding rules. Other government agencies have significantly more public participation than CDC, which is appropriate in a modern democracy.
  • Any practices and policies imposed in a public emergency, such as case definitions, definitions of measured quantities, data reporting practices, etc. imposed without public comment and review, should have an expiration date (e.g. sixty days) beyond which they must be subject to public review. Public comment, reviews, and cost/benefit analyses should happen during this emergency period.

Enacting these reforms should reduce the risk of serious errors, increase the quality and accuracy of CDC data and analyses, as well as any policies or CDC guidelines based on the data and analysis, and strengthen public confidence in the CDC and public health policies.

(C) 2021 by John F. McGowan, Ph.D.

About Me

John F. McGowan, Ph.D. solves problems using mathematics and mathematical software, including developing gesture recognition for touch devices, video compression and speech recognition technologies. He has extensive experience developing software in C, C++, MATLAB, Python, Visual Basic and many other programming languages. He has been a Visiting Scholar at HP Labs developing computer vision algorithms and software for mobile devices. He has worked as a contractor at NASA Ames Research Center involved in the research and development of image and video processing algorithms and technology. He has published articles on the origin and evolution of life, the exploration of Mars (anticipating the discovery of methane on Mars), and cheap access to space. He has a Ph.D. in physics from the University of Illinois at Urbana-Champaign and a B.S. in physics from the California Institute of Technology (Caltech).

[Video] How to Sterilize Groceries with a UV Light and Your Refrigerator

Click on title slide below to see video (on free speech friendly Odysee video platform).

https://odysee.com/@MathematicalSoftware:5/how_to_sterilize_groceries_with_a_uv_light_and_your_refrigerator:a

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Censored Video: YouTube

How to Sterilize Groceries with a UV Light and Your Refrigerator

Video shows how to sterilize groceries (or other items) with a UV light and your refrigerator.

NOTE: UV light can be dangerous. UV light can damage your eyes and skin. Prolonged exposure may cause skin cancer. Wear safety goggles, protective clothing, and minimize any exposure to UV light. Hopefully in future videos I will show how to turn on the UV light only when the refrigerator is completely closed. Wear gloves as shown in the video to protect from viruses and bacteria when handling the groceries or other items to be sterilized and to protect hands from the UV light.

A UV light with an ON/OFF switch on the power cord outside the refrigerator can be turned ON/OFF inside the refrigerator without exposure to the UV light. An RF (radio) remote control may be able to turn a UV light ON/OFF inside the closed refrigerator, unlike a standard IR (infrared) remote control.

Items mentioned:

ROHS UV “Corn Lamp”: https://www.amazon.com/Sanitizer-Disinfection-Germicidal-Restaurant-Supermarket/dp/B08LG9TX48/ref=sr_1_20?crid=1IEXUQM2MVD8O&dchild=1&keywords=uv+light+sanitizer&qid=1625589690&sprefix=uv+light+san%2Caps%2C265&sr=8-20

UV Safety Goggles: https://www.amazon.com/Tool-Klean-Safety-Glasses-Protection/dp/B081BHTJT8/ref=sr_1_2?dchild=1&keywords=uv+safety+goggles&qid=1625589772&sr=8-2

We don’t receive any sponsorship or consideration from the makers of the items demonstrated. There are many UV lights and many brands of UV protective eyewear and clothing. Do your own research and choose the best items for you.

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(C) 2021 by John F. McGowan, Ph.D.

About Me

John F. McGowan, Ph.D. solves problems using mathematics and mathematical software, including developing gesture recognition for touch devices, video compression and speech recognition technologies. He has extensive experience developing software in C, C++, MATLAB, Python, Visual Basic and many other programming languages. He has been a Visiting Scholar at HP Labs developing computer vision algorithms and software for mobile devices. He has worked as a contractor at NASA Ames Research Center involved in the research and development of image and video processing algorithms and technology. He has published articles on the origin and evolution of life, the exploration of Mars (anticipating the discovery of methane on Mars), and cheap access to space. He has a Ph.D. in physics from the University of Illinois at Urbana-Champaign and a B.S. in physics from the California Institute of Technology (Caltech).

[Video] How to Copy Data Tables into Working Python Code with EMACS Hotkey

https://odysee.com/@MathematicalSoftware:5/how_to_copy_data_tables_into_working_python_code_with_emacs_hotkey:5?

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HOW TO COPY AND PASTE DATA TABLES INTO WORKING PYTHON CODE WITH EMACS

Video shows how to select, copy, and paste text data tables into working Python code with the Emacs text and code editor’s rectangle mode and an EMACS hotkey.

EMACS HOTKEY CODE SHOWN: http://wordpress.jmcgowan.com/wp/code-functions-to-convert-a-text-data-table-to-working-python-code-in-emacs/

The Emacs text and code editor has a built in rectangle mode for selecting, copying, pasting, and maniuplating rectangular regions in text since Emacs 24.

https://www.gnu.org/software/emacs/manual/html_node/emacs/Rectangles.html

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(C) 2021 by John F. McGowan, Ph.D.

About Me

John F. McGowan, Ph.D. solves problems using mathematics and mathematical software, including developing gesture recognition for touch devices, video compression and speech recognition technologies. He has extensive experience developing software in C, C++, MATLAB, Python, Visual Basic and many other programming languages. He has been a Visiting Scholar at HP Labs developing computer vision algorithms and software for mobile devices. He has worked as a contractor at NASA Ames Research Center involved in the research and development of image and video processing algorithms and technology. He has published articles on the origin and evolution of life, the exploration of Mars (anticipating the discovery of methane on Mars), and cheap access to space. He has a Ph.D. in physics from the University of Illinois at Urbana-Champaign and a B.S. in physics from the California Institute of Technology (Caltech).

[Code] Functions to Convert a Text Data Table to Working Python Code in EMACS

This is EMACS code and text editor LISP code to convert a space delimited text table into working Python code in EMACS. By default, it binds the python-table function below to the CTRL-= key sequence in EMACS for easy use. Add this code to your dot emacs EMACS startup file (~/.emacs). Restart EMACS to load the new dot emacs startup file.

Select the text data table in EMACS using rectangle-mark-mode — bound to CTRL-SPACEBAR by default — and press CTRL-= to convert the selected text data table to a Python NumPy fast numerical array definition. The emacs LISP function python-table automates the steps shown in the previous post “[Video] How to Copy and Paste Data Tables into Working Python Code with EMACS.”

(defun python-table-rows (inputStr)
  "Convert space delimited text table to working Python fast NumPy array definition code"
  (interactive "e")
  (setq head "table_name = np.array([\n")
  ;; replace thousands separator comma (,) with Python separator underscore (_)
  (setq temp1 (replace-regexp-in-string "," "_" inputStr))
  ;; enclose each row in [ (row) ],
;;  (setq temp2 (replace-regexp-in-string "\\(.*\\)" "[\\1]," temp1))
  (setq temp2 (replace-regexp-in-string "\\([a-zA-Z0-9_ \\.]*\\)" "[\\1]," temp1))
  ;; remove any spurious [],
  (setq temp2b (replace-regexp-in-string "\\[\\]," "" temp2))
  ;; convert repeated space delimeters to (comma)(space)
  (setq temp3 (replace-regexp-in-string " +" ", " temp2b))
  ;;  add ] to close list of lists, close paren ) to convert to numpy array
  (setq tail "])")
  ;; build entire Python code block
  (setq temp4 (concat head temp3))
  (setq temp5 (concat temp4 tail))
  ) ;; end defun python-table

(defun python-table ()
  " convert selected region with space separated text table to python code "
  (interactive)
  (message "running python-table") ;; progress message
  ;; use buffer-substring-no-properties to strip fonts etc.
  ;; get text from selected region and put in tmp variable
  (setq tmp (buffer-substring-no-properties (mark) (point)))
  ;; convert to Python Code and put in table variable
  (setq table (python-table-rows tmp))
  (message table) ;; progress message
  (delete-region (mark) (point))  ;; remove the region contents
  (insert table)  ;; replace with python code table definition
  ) ;; end defun ptable()

(global-set-key (kbd "C-=") 'python-table) ;; bind python-table to hot key

Use CTRL-X CTRL-F ~/.emacs RETURN to read in, display, and edit your dot emacs file in the EMACS editor.

How to Use

Add this code to your dot emacs file. Modify as appropriate if you know what you are doing.

Restart emacs.

Use the EMACS rectangle-mark-mode command — usually bound to CTRL-X SPACEBAR in EMACS — to select a rectangular text table as in the text below. Use Edit Menu | Copy or ESC-W to copy the text data table in EMACS.

This is an example of selecting, copying, and pasting a text table
into Python source code using the Emacs code and text editor.

Python is a popular programming language. With add on packages
such as NumPy, SciPy, and Matplotlib, it is a leading tool
for data analysis, scientific and numerical programming.

Demo Text Table
0.5      276
2.5      328
6.5      134
12.0     139
17.0   1,807 random notes here
22.0   3,342
29.5   5,340 commentary here
39.5   3,316
49.5   2,106
59.5   1,360 doodling here
69.5     562
79.5     270
89.5     120

This data is so great.

Three ways to select a rectangular text region in Emacs:

ESC-x rectangle-mark-mode
CTRL-X SPACE
SHIFT-(mouse drag)

URL: http://www.mathematical-software.com/

Use CTRL-y to paste the selected text table into Python code. Select the table and use CTRL-= to convert to working Python source code — a NumPy fast array definition with the values from the text table. The Python add-on packages NumPy, SciPy and MatPlotLib provide extensive numerical and statistical analysis functions and plotting.

(C) 2021 by John F. McGowan, Ph.D.

About Me

John F. McGowan, Ph.D. solves problems using mathematics and mathematical software, including developing gesture recognition for touch devices, video compression and speech recognition technologies. He has extensive experience developing software in C, C++, MATLAB, Python, Visual Basic and many other programming languages. He has been a Visiting Scholar at HP Labs developing computer vision algorithms and software for mobile devices. He has worked as a contractor at NASA Ames Research Center involved in the research and development of image and video processing algorithms and technology. He has published articles on the origin and evolution of life, the exploration of Mars (anticipating the discovery of methane on Mars), and cheap access to space. He has a Ph.D. in physics from the University of Illinois at Urbana-Champaign and a B.S. in physics from the California Institute of Technology (Caltech).