def run():
"""
Run Monte Carlo simulations with different numbers
of trials to estimate the expectation that you will
get 3-of-a-kind when rolling 5 dice.
Actual probability of 3-of-a-kind: .1929
"""
trial_sizes = [10, 100, 1000, 10000, 100000]
estimates = []
for ntrials in trial_sizes:
estimates.append((ntrials, monte_carlo(ntrials)))
for ntrials, est in estimates:
print ntrials, ":", est
log_estimates = [(math.log(ntrials, 10), est)
for ntrials, est in estimates]
simpleplot.plot_bars("3-of-a-Kind", 400, 300,
"Log(Trials)", "Expectation",
[log_estimates])
# Uncomment to run
#run()
##=================================================================
##=================================================================
def main(): # type: () -> None
"""Open a plot."""
datalist = [(1, 2), (2, 3), (5, 4), (8, 3), (9, 2)]
dataset = {1: 3, 2: 4, 5: 5, 8: 4, 9: 3}
filename = None
if SIMPLEGUICS2PYGAME:
from sys import argv # pylint: disable=import-outside-toplevel
if len(argv) == 2:
filename = argv[1]
if filename is None:
simpleplot.plot_bars('Test plot_bars', 400, 400, 'x', 'y',
(datalist, dataset), ('datalist', 'dataset'))
else:
simpleplot.plot_bars('Test plot_bars',
400,
400,
'x',
'y', (datalist, dataset), ('datalist', 'dataset'),
_filename=filename)
if SIMPLEGUICS2PYGAME and (len(argv) != 2):
simpleplot._block() # pylint: disable=protected-access
def plot_indeg_distribution(graph_dictionary, ref_str, plot_type):
"""
Part 1: graphs the log-log plot of the
normalized in-degree distribution for
the input dictionary called ref_str
"""
# gets the unnormalized distribution and normalizes it
graph_dist = deg_analysis.in_degree_distribution(graph_dictionary)
sum_total = 0.0
for key in graph_dist.keys():
sum_total += graph_dist[key]
graph_dist_norm = {}
for key in graph_dist.keys():
graph_dist_norm[key] = graph_dist[key]/sum_total
# creates the log/log data set
graph_dist_log = {}
for key in graph_dist_norm.keys():
graph_dist_log[math.log(key, 10)] = math.log(graph_dist_norm[key], 10)
# graph types
if plot_type == "LOGLOG":
simpleplot.plot_scatter("LogLog of Normalized In-Degree Distribution for " + ref_str,
800, 600, "Log Degree", "Log Amount", [graph_dist_log])
elif plot_type == "NORMAL":
simpleplot.plot_scatter("Normalized In-Degree Distribution for " + ref_str,
800, 600, "Degree", "Amount", [graph_dist_norm])
elif plot_type == "BARS":
simpleplot.plot_bars("Bar Plot of Normalized In-Degree Distribution for " + ref_str,
800, 600, "Degree", "Amount", [graph_dist_norm])
def question4():
""" create hypothesis testing distribution """
null_dist = {}
if DESKTOP:
# load sequences and scoring matrix
score_matrix = read_scoring_matrix(PAM50_URL)
human_eyeless = read_protein(HUMAN_EYELESS_URL)
fruitfly_eyeless = read_protein(FRUITFLY_EYELESS_URL)
# generate the null distribution
null_dist = generate_null_distribution(human_eyeless, fruitfly_eyeless, score_matrix, 1000)
else:
# previously calculated using desktop python
null_dist = {38: 1, 39: 1, 40: 6, 41: 8, 42: 17, 43: 31, 44: 41, 45: 47, 46: 65, 47: 76,
48: 66, 49: 63, 50: 62, 51: 69, 52: 69, 53: 57, 54: 49, 55: 37, 56: 26, 57: 30,
58: 33, 59: 23, 60: 25, 61: 20, 62: 5, 63: 11, 64: 11, 65: 7, 66: 5, 67: 4,
68: 5, 69: 4, 70: 5, 71: 3, 72: 3, 73: 4, 74: 1, 75: 2, 80: 3, 81: 1, 82: 1,
85: 1, 87: 1, 92: 1}
# normalize null_dist
for key in null_dist.keys():
null_dist[key] /= float(1000)
simpleplot.plot_bars("Normalized Null Distribution of Human and Fruitfly Local Alignment (n=1000 trials)",
800, 600, "Local Alignment Score", "Distribution", [null_dist])
def run():
"""
Run Monte Carlo simulations with different numbers
of trials to estimate the expectation that you will
get 3-of-a-kind when rolling 5 dice.
Actual probability of 3-of-a-kind: .1929
"""
trial_sizes = [10, 100, 1000, 10000, 100000]
estimates = []
for ntrials in trial_sizes:
estimates.append((ntrials, monte_carlo(ntrials)))
for ntrials, est in estimates:
print ntrials, ":", est
log_estimates = [(math.log(ntrials, 10), est)
for ntrials, est in estimates]
simpleplot.plot_bars("3-of-a-Kind", 400, 300,
"Log(Trials)", "Expectation",
[log_estimates])
def run():
"""
Load a graph and play the Kevin Bacon Game.
"""
graph5000 = movies.load_graph('subgraph5000')
if len(graph5000.nodes()) > 0:
# You can/should use smaller graphs and other actors while
# developing and testing your code.
play_kevin_bacon_game(graph5000, 'Kevin Bacon',
['Amy Adams', 'Andrew Garfield', 'Anne Hathaway', 'Barack Obama', \
'Benedict Cumberbatch', 'Chris Pine', 'Daniel Radcliffe', \
'Jennifer Aniston', 'Joseph Gordon-Levitt', 'Morgan Freeman', \
'Sandra Bullock', 'Tina Fey'])
# Plot distance histograms
for person in ['Kevin Bacon', 'Stephanie Fratus']:
hist = distance_histogram(graph5000, person)
simpleplot.plot_bars(person, 400, 300, 'Distance', \
'Frequency', [hist], ["distance frequency"])
import simpleplot
dataset1 = {3: 5, 8: 2, 1: 3}
dataset2 = [(1, 2), (4, 7), (2, 5), (7, 6)]
simpleplot.plot_bars('Sample', 400, 300, 'x', 'y',
[dataset1, dataset2], ['dataset1', 'dataset2'])
def plot_hist_data(score_list):
"""
Shows a plot of the histogram data returned by hist_data().
"""
simpleplot.plot_bars("Histogram", 400, 300, "range", "count",
[zip([0, 20, 40, 60, 80], hist_data(score_list))])
PYTHON_VERSION = 'Python ' + python_version.split()[0]
MATPLOTLIB_VERSION = 'matplotlib ' + matplotlib_version
else:
PYTHON_VERSION = 'CodeSkulptor' # http://www.codeskulptor.org/
MATPLOTLIB_VERSION = ''
datalist = [(1, 2), (2, 3), (5, 4), (8, 3), (9, 2)]
dataset = {1: 3, 2: 4, 5: 5, 8: 4, 9: 3}
filename = None
if SIMPLEGUICS2PYGAME:
from sys import argv
if len(argv) == 2:
filename = argv[1]
if filename is None:
simpleplot.plot_bars('Test plot_bars', 400, 400, 'x', 'y',
(datalist, dataset),
('datalist', 'dataset'))
else:
simpleplot.plot_bars('Test plot_bars', 400, 400, 'x', 'y',
(datalist, dataset),
('datalist', 'dataset'),
_filename=filename)
if SIMPLEGUICS2PYGAME and (len(argv) != 2):
simpleplot._block()
PYTHON_VERSION = 'Python ' + python_version.split()[0]
MATPLOTLIB_VERSION = 'matplotlib ' + matplotlib_version
else:
PYTHON_VERSION = 'CodeSkulptor' # http://www.codeskulptor.org/
MATPLOTLIB_VERSION = ''
datalist = [(1, 2), (2, 3), (5, 4), (8, 3), (9, 2)]
dataset = {1: 3, 2: 4, 5: 5, 8: 4, 9: 3}
filename = None
if SIMPLEGUICS2PYGAME:
from sys import argv
if len(argv) == 2:
filename = argv[1]
if filename is None:
simpleplot.plot_bars('Test', 400, 400, 'x', 'y', (datalist, dataset),
('datalist', 'dataset'))
else:
simpleplot.plot_bars('Test',
400,
400,
'x',
'y', (datalist, dataset), ('datalist', 'dataset'),
_filename=filename)
if SIMPLEGUICS2PYGAME and (len(argv) != 2):
simpleplot._block()
