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/

Read more: Research on Exercise and Nutrition

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.

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Fear

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Last Updated: 07 April 2020

I grew up in an imperfect household, like many, if not most of us. One of the lessons clearly instilled in me at an early age was that responsibility and duty are primary. I read the Odyssey at 12. Odysseus had a primary responsibilty to return to Ithaca. I started the Iliad, but could not find my way to go through it. I could not experience the origin of Western literature. It was just too hard, too coldly violent for me to experience. I have picked up the book several times over my 60 years, it remains true to today. I cannot read it. I understand that the Classical Greeks, the people that gave birth to the entire Western Civilization, were an unimaginably hard people.

In 9th grade, my English teacher used to select passages of literature, have us read them, and then do a writing assignment describing our reaction to them. Looking back all those decades, I know there must have been dozens of assignments. But the 14 year old boy only remembered one, which I quote, probably with small errors, here: “Cowards die many times before their deaths. The valiant only taste of death but once. Of all the strange things I have ever heard, it seems to me most strange that men fear death, given that death, a necessary end, will come when it will come.” (Shakespeare’s Julius Caesar)

We get one journey through life. After that, nothing. For most of us, we may be remembered by the generation that succeeds us, but thereafter, our lives are consigned to be utterly unremembered.

Given inescapable annihilation, we must choose how to create ourselves on the canvas of a lifetime. We can chose to do it with a nod to excellence, an embrace of responsibility and duty, and a touch of panache. The alternative is to amount to nothing.

Chinese Tilapia

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Last Updated: 07 April 2020

 Denise's "Chinese" tilapia recipie.

	First you prepare the  aromatics: finely sliced leeks chopped cilantro ginger paste
	Sautee the tilapia in canola oil.
	Then you sautee them in the oil from the tilapia, letting them steam. After a minute or so, add soy sauce and quar gum to thicken.
	While the aromatics cook, gently steam some bok choy.
	Plate the dinner, placing the aromatics on the tilapia.