Global Warming and Capitalism.

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Last Updated: 31 July 2021

Thinking about capitalism, shareholder capitalism, economic expansion, consumerism, and global warming. 

The American Heritage Dictionary defines capitalism as "an economic system in which the means of production and distribution are privately or corporately owned and development occurs through the accumulation and reinvestment of profits gained in a free market." As such, capitalism has existed since before the dawn of history as farmers, hunters, and individual merchants traded their wares for necessities. This straightforward activity reached a zenith with the great trading cities of Italy at the beginning of the Renaissance, Venice, Genoa, and Florence. These formed banks to fund increasingly longer, riskier trading expeditions that brought back great wealth reaching as far as China. While wealth was accumulated, it tended to be acquired by families and city-states.

The Eighteenth Century brought about two tectonic shifts to economies and the accumulations of wealth. The Industrial Revolution created enormous potential to manufacture and distribute masses of goods at economies of scale never seen in history. Financing such endeavors were beyond even the banks and a new way to generate capital was created. Shareholder capitalism is " a system driven by the interests of shareholder-backed and market-fixated companies". The Industrial Revolution was fueled by carbon combustibles. This was the beginning of the now inexorable growth of CO2 emissions that drives climate change.  But one other innovation was added to seal the coffin of the world environment that I knew as a boy in the 1960's. Henry Ford believed in paying his worker a decent wage so that workers could go out and buy one of the all-black cars Ford Motor Company was producing. Alfred P. Sloan, CEO of General Motors, had a better idea. He splintered the company into five brands based on price range. He introduced color to cars and, most importantly, he changed their styling each year. Sloan took Ford's transportation solution and made it stylish, selling new models to the consumer every year.

Sloan had invented consumerism, a social and economic order that encourages the acquisition of goods and services in ever-increasing amounts. Consumers no longer bought what they needed. They bought what they wanted. Madison Avenue was born to create the desire for the things that Wall Street created. The endless, insatiable desire was created. This created a cycle. Companies that made stuff, big and small, consumable and capital, invested large amounts of money in advertising to create the desire for current and future products. 

After WWII, G.I.'s coming home enjoyed their newfound middle-class status taking advantage of the G.I. bill and other rewards of surviving the carnage. They encountered shareholder capitalism using consumerism to fuel product sales. The ugly fact about shareholder capitalism is that stock prices are driven by quarterly profits. Profits are expected to increase quarterly. The only way to increase profits is to drive consumer demand via consumerism and decrease salaries. Increased consumer demand requires increased production. We went from homes and cars to stoves and refrigerators to portable electronics and personal devices to subscriptions to entertainment that consumers never even own. And all the while, we consume a steadily increasing flow of natural resources, produce ever-increasing pollution, and accelerate the heat civilization causes via direct production and greenhouse gas emissions. 

Aside from the measurable deforestation, consumption of non-replaceable resources, and steady increase in the world's temperature and violence of storms, I have seen the change on a micro-level. The fireflies that used to turn Maryland pastures and lawns into a starry night sky are in the 1990s are completely gone. As little as four years ago, late summer would bring clouds of monarchs around lantana. Now, I may see one or two over the entire summer. My personal impression that the number of birds has declined rapidly since my youth is unhappily confirmed by Scientific American. The world is both changing and dying because consumers think that they need a new iPhone every year, need to live in a 65F to 75F temperature range, a new car every 6 years....the list is endless.

Greta Thunberg's delusion of infinite economic growth.

The End of Growth Wouldn't Be the End of Capitalism

Global warming vs. climate change.

between 13,500 miles and 93,000 miles: electric vehicle vs. a Corolla

The Enchiridion by Epictetus

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Last Updated: 25 December 2020

As in a voyage, when the ship is at anchor, if you go on shore to get water, you may amuse yourself with picking up a shellfish or a truffle in your way, but your thoughts ought to be bent toward the ship, and perpetually attentive, lest the captain should call, and then you must leave all these things, that you may not have to be carried on board the vessel, bound like a sheep; thus likewise in life, if, instead of a truffle or shellfish, such a thing as a wife or a child be granted you, there is no objection; but if the captain calls, run to the ship, leave all these things, and never look behind. But if you are old, never go far from the ship, lest you should be missing when called for.

Epictetus

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The Captain

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Last Updated: 05 November 2020

I feel for those who struggle, whose numbers have grown so much in 2020.

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Template for Photos

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Last Updated: 04 November 2020

boooo

 test video

Yak

The Food We Eat

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Last Updated: 03 October 2020

I have wondered why it was so easy for me to give up the eating tastes and habits that most Americans find so compelling and ultimately cause most of us to become overweight or obese. My widowed Issei mother’s influence and early years so unwillingly spent on the Italian Riviera removed me from the pernicious influence of Big Food’s brainwashing advertising and addictive products. Mom was always suspicious of packaged foods. Interestingly, her objections had an almost moral angle. She would say that people who indulged in fast food and frozen dinners were too lazy to prepare real meals. But she also seemed to have an instinct for healthy eating. She knew that sugar and excess fat, particularly saturated fats, were bad. For me, a Coke was a rare treat, as with my beloved Fritos and fish sticks. Sugar frosted flakes, on the other hand, were completely out. She fully understood the value of lightly cooked vegetables and fruit, though her preference was based on taste rather than health. I remember her as a small, seemingly frail woman, who was remarkably healthy and with a will strong enough to stop smoking in the early 1960’s quickly when I started to bring home the ant-smoking messaging that I was getting in school. Years later, I came to suspect that she had experienced a bout of anorexia during time spent away from her home in Japan at a boarding school in England. She was miserable away from her family and home in Japan. I think this experience, along with being exposed to a traditional Japanese diet in her youth, influenced her food preferences. This was the genesis for her distaste for all food that nutritionists now refer to as hyperpalatable.

The other dimension to my immunity to Big Food’s influences is the several years that I spent in a small coastal town in Italy, far away from the reach of their advertising and junk products. I grew up inculcated by the Mediterranean diet. It was the reality of being in that location in the early 1960’s. I spent those key formative years acquiring a different sense of normal eating.

I was further aided by two other factors in avoiding the psychological and physical addition to Big Food. First, as an adult, I began to realize something was wrong with the diet pushed by Big Food while taking Vertebrate Zoology in college circa 1975. I knew that it is no evolutionary coincidence that humans have the highest density of sweat producing glands in our skin of any mammal. It is no coincidence that this trait is combined with bipedalism and all the advantages locomotion on two legs confers to running. Running under the hot sun at mid-day across the undeveloped portion of the University of Florida campus, it became obvious to me that humans were born to run and to run in the heat. I reasoned, not quite correctly, that healthiest and happiest, we need to live and eat as simply and naturally as possible. As long as we remained foragers, our ancestors’ brains remained small. When the Central African forests became savanna, our line of primate ancestors came down from the trees and began to roam. Bipedalism is a more efficient mode to traverse distance. When we became bipedal, we became hunters. The additional calories from meat allowed selection for larger brain size; larger brain size afforded a selective advantage to hunting. The thoughtful predator is more successful: thus, the ascent of man.

At 21, I didn’t take this line of thought much further, but I came away from Vertebrate Zoology with a firm understanding of H. Sapiens’ place in nature, the web of life, and the evolution of animals. The nearly four years as a zoology major taught me something else: an understanding of the value of peer-reviewed research. This laid the basis for my easy dismissal of so many who now proclaim some new dietary rubbish. If it is not evidence-based, then it is useless, usually self-promoting, speculation. If the diet guru is selling something, that prima fascie evidence of fraud.

Of course, during the 10 years after my return to the United States in my my late adolescence, the prevalence of Big Food’s advertising and availability pulled me into the bad habits that are universal today. I was a chunky teen. But by my early 20’s, informed by my experience in zoology, I had taken up running and laco-ovo-vegetarianism. Of course, my fitness and leaness grew, enhanced by years in the crucible that is the Marine Corps. However, this lasted until I became a professional, once again exposed to all the cultural influences that almost inevitably lead to growing waistlines with age. By 49, I was nearly clinically obese and my family practitioner was cautioning me that I “could expect my first heart attack within two years”.

In response, I ended my nearly 30 year hiatus from running and tried to clean up my diet. The zoology background formed a foundation to my approach: seeking peer-reviewed information on proper diet. My foray into the science left me with the impression that nutrition research and the typical grocery store must exist in parallel, mutually exclusive, universes. Almost everything in the stores is some sort of recombination of refined grains, fat, and salt. The mammalian meats, far from being remotely similar to what our ancestors ate, are full of antibiotics, hormones, and laced with fat from being unnaturally constrained under abusive circumstances. I quit all of that and embraced a holistic combination of running, strength training, and eating as healthily as I know how to do. I am hugely satisfied with the results.

A set of heuristics have guided me:

• Sugar, like alcohol, is a poison.

• Typical industrial meat is completely unnatural and nearly poisonous. As much as possible, grain-fed, free range only. No processed meats: they are poison. Prefer chicken to beef, fish to chicken, vegetarian protein source to fish. Limit flesh to one portion per day. Fry nothing.

• As much lightly cooked vegetables and unprocessed fruits as you wish.

• Whole grains only. Prefer legume carbs to grain carbs, but do not exclude the latter.

• Salt, sugar, and fat are the basic ingredients to poisonous foods: avoid anything where these ingredients have been artificially added.

• It is not natural to eat sugary foods. It is not natural to eat salty foods. It is not natural to eat fatty foods (unless you’re living in the tundra). Our bodies cannot properly digest them and they poison us.

• Humans are structured to perform a lot of movement. If this is not done, our metabolism degrades rapidly with age.

• Trust nothing on a restaurant menu. Shun items that are blended or mixed to the point where original ingredients cannot be discerned. Order nothing that doesn’t have an associated calorie count on the menu. If you’re not a serious endurance or strength athlete, you have no business consuming a 700 calorie meal.

• Read packaging labels; pick ones with the shortest list of unpronounceable things. Unless it’s olive oil or avocado, greater than 30% calories from fat is a no-go. Likewise, greater than 300 or 400 milligrams of sodium per serving is also a no-go. While you’re at it

References:

 https://www.news-medical.net/health/How-Fast-Food-Affects-Childrens-Health.aspx

 https://www.apa.org/topics/kids-media/food#

 https://jamanetwork.com/journals/jama/fullarticle/199990

 https://www.webmd.com/diet/news/20041214/for-immigrants-america-is-land-of-obesity

 https://www.pnas.org/content/112/32/9932

 http://barefootrunning.fas.harvard.edu/

 https://www.facebook.com/instituteforfunctionalmedicine/posts/ifm-member-dallas-hartwig-is-a-certified-physical-therapist-and-co-founder-of-th/10153139812589471/

https://en.wikipedia.org/wiki/Functional_medicine#Institute_for_Functional_Medicine

https://www.reddit.com/r/whole30/comments/7u4pr2/whole30_sports_nutrition_certifications/

https://osher.ucsf.edu/patient-care/integrative-medicine-resources/cancer-and-nutrition/faq/animal-protein-cancer-risk

https://scholar.google.com/scholar?hl=en&as_sdt=0%2C21&q=salt+sugar+fat&btnG=

https://time.com/4087775/sugar-is-definitely-toxic-a-new-study-says/

 https://www.onlinejacc.org/content/76/12/1484

Research on Exercise and Nutrition

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Last Updated: 25 September 2020

The full text is published by NIH. Interestingly, the subjects were housed and monitored in a clinical setting with no exercise prescribed. Also, no smoking or alcohol were allowed. The results confirm what the bodybuilding community has known for half a century: mere consumption of protein is anabolic. Of course consumption of protein with exercise is even more so.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3777747/

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Pre-Run

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Last Updated: 16 August 2020

No part of me wants to go out there this morning. It is already 75 F and 0615. I'm late for my run on the hottest morning of the year. I have coffee and a slice of bread with peanut butter. I remember yesterday's 7 miler. To run like that is to completely embrace sweat. It runs down my entire body, stinging my eyes. It is an embrace of what humans evolved to be. We evolved sweat glands as we chased prey across the savanna. But upright, we have an advantage. With each gallop, our target's hind legs come forward together, compressing its abdominal cavity and pushing up against its diaphragm. This compresses the area available for the lungs to expand, hindering its inhalation. Humans, however, run unimpeded, with the additional benefit that we can carry weapons with our non-running limbs to make up for our lack of claws and teeth.

It is hot out there, in contrast to my dry home, cooled to a comfortable 76 F. But being cool and comfortable is not natural. It is not even healthy.

21st Century Genocide

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Last Updated: 16 August 2020

Pandas Min Max

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Last Updated: 14 August 2020

Assignment2 Weather-Dates on X Axis - Submitted

 

Assignment 2¶

Before working on this assignment please read these instructions fully. In the submission area, you will notice that you can click the link to Preview the Grading for each step of the assignment. This is the criteria that will be used for peer grading. Please familiarize yourself with the criteria before beginning the assignment.

An NOAA dataset has been stored in the file data/C2A2_data/BinnedCsvs_d400/fb441e62df2d58994928907a91895ec62c2c42e6cd075c2700843b89.csv. This is the dataset to use for this assignment. Note: The data for this assignment comes from a subset of The National Centers for Environmental Information (NCEI) Daily Global Historical Climatology Network (GHCN-Daily). The GHCN-Daily is comprised of daily climate records from thousands of land surface stations across the globe.

Each row in the assignment datafile corresponds to a single observation.

The following variables are provided to you:

• id : station identification code
• date : date in YYYY-MM-DD format (e.g. 2012-01-24 = January 24, 2012)
• element : indicator of element type
  • TMAX : Maximum temperature (tenths of degrees C)
  • TMIN : Minimum temperature (tenths of degrees C)
• value : data value for element (tenths of degrees C)

For this assignment, you must:

1. Read the documentation and familiarize yourself with the dataset, then write some python code which returns a line graph of the record high and record low temperatures by day of the year over the period 2005-2014. The area between the record high and record low temperatures for each day should be shaded.
2. Overlay a scatter of the 2015 data for any points (highs and lows) for which the ten year record (2005-2014) record high or record low was broken in 2015.
3. Watch out for leap days (i.e. February 29th), it is reasonable to remove these points from the dataset for the purpose of this visualization.
4. Make the visual nice! Leverage principles from the first module in this course when developing your solution. Consider issues such as legends, labels, and chart junk.

The data you have been given is near Ann Arbor, Michigan, United States, and the stations the data comes from are shown on the map below.

In [1]:

#%matplotlib notebook

import matplotlib.pyplot as plt
import matplotlib as mpl
#mpl.get_backend()
import matplotlib.dates as mdates

import pandas as pd
import numpy as np
import datetime as dt

data_file = "fb441e62df2d58994928907a91895ec62c2c42e6cd075c2700843b89.csv"
raw_data = pd.read_csv(data_file)
raw_data.to_csv("raw_data.csv")

#print(raw_data.describe())
#print(raw_data.head())


# take the inverse of the mask to delete all leap days
raw_data = raw_data[~raw_data['Date'].str.contains("02-29")]

# separate 2005-2014 data and 2015 data
data_5_14 = raw_data[raw_data['Date'] < '2015-01-01' ]
data_2015 = raw_data[raw_data['Date'] > '2014-12-31']
#print(data_2015.head())

# separate TMAX data from TMIN data
max = data_5_14[data_5_14['Element']=='TMAX']
#max.to_csv('max.csv')
min = data_5_14[data_5_14['Element']=='TMIN']

# find max and min values for each date from among the
# measuring stations
max = max.groupby(['Date']).max()
min = min.groupby(['Date']).min()
data_2015_max = data_2015.groupby(['Date']).max()
data_2015_min = data_2015.groupby(['Date']).min()
data_2015 = data_2015_max.append(data_2015_min)
#print('new data_2015: \n', data_2015.describe())
#max.reset_index(inplace=True)
#max.to_csv('max_after_groupby.csv')

# drop unnecessary columns
max = max.drop(columns=['ID','Element'])
min = min.drop(columns=['ID','Element'])
data_2015.drop(columns=['ID','Element'])

# get the absolute highs and lows
max_high_temp = max.max()[0]
min_low_temp = min.min()[0]
#print('max_high_temp: ', max_high_temp)
#print('min_low_temp: ', min_low_temp)

# remove 20115 data between max_high_temp and min_low_temp
data_2015 = data_2015[(data_2015['Data_Value'] > max_high_temp) 
                      | (data_2015['Data_Value'] < min_low_temp)]

#print("for scatter plot: \n",data_2015)


# prepare the dataframes for plotting
max = max.reset_index()
min = min.reset_index()
data_2015 = data_2015.reset_index()

#print('max: \n', max.describe())
#print('min: \n', min.describe())
#print('data_2015: \n', data_2015.describe())

max_values = max['Data_Value'].to_numpy()
max_values = max_values / 10
max_dates = max['Date'].to_numpy()

# convert np array of string dates to list of datetime.dates
max_dates = \
    [dt.datetime.strptime(d,'%Y-%m-%d').date() for d in max_dates]

#print(max_dates)
#print(type(max_dates))
min_values = min['Data_Value'].to_numpy()
min_values = min_values / 10
min_dates = min['Date'].to_numpy()

# convert np array of string dates to list of datetime.dates
min_dates = \
    [dt.datetime.strptime(d,'%Y-%m-%d').date() for d in min_dates]

print("888888888", max_dates[0], max_dates[-1])
data_2015_values = data_2015['Data_Value'].to_numpy()
data_2015_values = data_2015_values / 10
data_2015_dates = data_2015['Date'].to_numpy()

print("******", type(data_2015_dates[0]))


#
# First Axes.....
#

first_axes = plt.gca()
x = first_axes.xaxis

# rotate the tick labels for the x axis
for item in x.get_ticklabels():
    item.set_rotation(-45)

first_axes.set_xlabel('\n Daily Highs and Lows 2004 through 2014')
first_axes.set_ylabel('Temperature (C)')
first_axes.set_title('2015 Temperatures that Exceed Maximum Highs and Lows from \
2004 through 2014 \n')

#first_axes.set_xlim(left=min_dates.min(), right=min_dates.max())

first_axes.xaxis.set_major_formatter(mdates.DateFormatter('%m/%d/%Y'))
first_axes.xaxis.set_major_locator(mdates.AutoDateLocator())
first_axes.set_ylim(top=70, bottom=-40)

plt.subplots_adjust(bottom=0.25, top=0.7)
f_axes_max_line, = first_axes.plot(max_dates, 
                                  max_values, 
                                  'r', 
                                  label='Maximum', 
                                  alpha=0.5, 
                                  linewidth=0.5)


f_axes_min_line, = first_axes.plot(min_dates, 
                                  min_values, 
                                  'blue', 
                                  label='Minimum', 
                                  alpha=0.5, 
                                  linewidth=0.5)

first_axes.fill_between(max_dates, max_values, min_values, facecolor='blue', alpha=0.25)

plt.subplots_adjust(bottom=0.25, top=0.55)
#
# build second axes
#


second_axes = first_axes.twiny() 
second_axes.set_xlabel("2015 Temps Exceeding 2004 to 2014 Highs and Lows\n")

second_axes.xaxis.set_major_formatter(mdates.DateFormatter('%m/%d/%Y'))
second_axes.xaxis.set_major_locator(mdates.DayLocator())

left = mpl.dates.datestr2num('2015-02-01')
right = mpl.dates.datestr2num('2015-02-28')
#left = mpl.dates.datestr2num('2015-01-01')
#right = mpl.dates.datestr2num('2015-12-31')
second_axes.set_xlim(left=left, right=right)
# rotate the tick labels for the upper x axis

# rotate the axis labels
top_x = second_axes.xaxis
for item in top_x.get_ticklabels():
    item.set_rotation(315)

# reduce the number of xticks to every other xtick in February
xticks = second_axes.get_xticks()
xticks = xticks[0::2]
second_axes.set_xticks(xticks)

#print(data_2015_dates)
#print(type(data_2015_dates[0]))
data_2015_dates_num = mpl.dates.datestr2num(data_2015_dates)
print(data_2015_dates[0])
print(type(data_2015_dates[0]))

'''
second_axes.annotate(s='some string', 
                     xy=(data_2015_dates[0],data_2015_values[0]), 
                     xytext=(-10.0,0.0),
                     textcoords='offset points',
                     ha='right')
'''
s_axis_scatter = second_axes.scatter(
    data_2015_dates_num, 
    data_2015_values) 

plt.legend([f_axes_max_line,f_axes_min_line, s_axis_scatter],
           ("2004/14 Max Temps",'2004/14 Min Temps','2015 Extremes'),
           loc='upper left',
           fontsize='small')

fig = plt.gcf()
fig.set_size_inches(10, 10)
#plt.gcf().autofmt_xdate()

#host.yaxis.get_label().set_color(p1.get_color())
#leg.texts[0].set_color(p1.get_color())

#par.yaxis.get_label().set_color(p2.get_color())
#leg.texts[1].set_color(p2.get_color())

 

888888888 2005-01-01 2014-12-31
****** <class 'str'>
2015-02-20
<class 'str'>

 

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Fascism

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Last Updated: 16 June 2020

Yes, Trumpism really is fascism.

Read more: Fascism