The poems below were created using Python coding and a Markov chain text generator. The text corpus being parsed and rearranged by the Markov chain text generator consists of a library of poems by English Romantic poet, John Keats, and a library of poems by Hunter Stabler. The Python coding used to generate the poems is embedded below as well.

Songs of mine

What is more soothing to me than these songs of mine?

Who shall his fame impair when thou art capable,
As thou exceedest all things in thy silent mind?
Unbounded knowledge makes a God as wrathful as himself.
Thou hath smelteth surely dealteth
unholy effervescent vespers of the swine

Redolent of sulfur twine
round esters offside apothecary shelf
tremors imbibe
a crescent indigo swill

calming ghostly pipes
monotrope amethyst sleep;
orbs blink and soften
Esper's crystal eye.

A pauper's coffin
from billowy vagueness shies

Unravish'd bride of quietness,
Thou foster-child of silence
and slowly animated through clouds of divinorum
and of morn float in voluptuous fleeces o'er the quartz clearness,
to woo its own sad image into nearness:
Death to light you, or mourn the light of death to show you,
beryl fissure leaks like thin silver streaks across the sky,
And I will tell thee what thou wouldst have been?

Nay, a blackest shape
reconciled internally

horselike tourmaline

Knowing now each piece

Not waiting until after to learn what it has to teach

For what a height my spirit leaps, and prances,
E'en then my soul with exultation dances
for that finality from outside perspective glances.

Shut up senses, telesthesiastic gyres
wait in the stillness.
Finally from inside perspective smote
reckoning prescient illness

a rattling that unhinged the mind from time

What bezel lens through the bee-mouth sips:
Ay, in those meads where sometimes secrets to the puzzle finds

endgame buzzer ringing tines

echoes in a compound eye

O let me taste that sweet sound once more,
the thought after thought to nourish uphill gyroscopic spires
trickle down from cathedral vaulting antipodal pole of the hivemind

involuntarily

A Testuary Exegesis

Only you have the goodliest view of this sweet spot, Some fainter gleamings o'er his book: 
Who had power to make me sick of joy and pain; 
Clasp'd like a milk-white lamb that bleats for man's protection.

What does he murmur with his maid, And to his eyes; And from the far-foamed sands.

Then in a bed of snow.

But what is fair?

Gentle windings to the tender greening of April meddles.

This passion lifted upon his arm he lean'd; not rising, from supreme contempt.

Silent entangler of a leafy world 
we rest in hope to see 
no other breezes than are blown through verdurous glooms and winding mossy ways.

Let the mad poets say whate'er they please of the leaves hast never told 
how, from a land of fragrance, quietness, and trees, and flowers mold.

Then, once again, the charmed God began an oath, 
and through the water round that bend; 
Not the minutest whisper does it send 
To the wide-spreaded night above her towers end.

At a touch sweet pleasure melteth like bubbles when rain pelteth; 
Then let winged fancy wander through the thought of every guest
that each had found a little noiseless noise among the em'rald tresses; 
While the ermine let music wander skittering round my ears.

”’ python

#!/usr/bin/env python2

–– coding: utf-8 ––

“”” Created on Tue Oct 2 19:22:14 2018

@author: hunterstabler “”” #mylist = [] i #for i in range(0,5):

my_list.append(synthesized_model.make_sentence(140))

import markovify

with open (“hunter_poems.txt”) as file: eas = file.read()

with open (“keats_02_edited.txt”) as file: keats2 = file.read()

with open (“keats_edited.txt”) as file: keats = file.read()

with open (“palace_of_pleasure.txt”) as file: p_o_p = file.read()

power_model = markovify.Text(eas) bull_model = markovify.Text(keats2) pag_model = markovify.Text(keats) pal_model = markovify.Text(p_o_p)

synthesized_model = markovify.combine([power_model, bull_model, pag_model],[10,1,1])

#for i in range(3):

print power_model.make_sentence()

#print " "

poem = synthesized_model.make_sentence()

#with open(“poem_experiment_class.md”, “w”) as f:

f.write(“## Title”)

f.write(“\n”)

f.write(“”'”)

f.write(“\n”)

f.write(poem)

f.write(“\n”)

f.write(“”'”)

with open(“keats_poem_10.md”, “a”) as f: f.write(“””

{poem}

“””.format(poem=poem))