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Add python_ta check to visualization.py

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MstrPikachu
2021-12-13 22:51:04 -05:00
parent a904e91058
commit 882360b819
1 changed files with 51 additions and 26 deletions
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src/visualization.py
+51 -26
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@@ -15,6 +15,9 @@ import matplotlib.ticker
import scipy.signal
from matplotlib import pyplot as plt, font_manager
import python_ta
import python_ta.contracts
from collect_others import get_covid_cases_us
from constants import RES_DIR, REPORT_DIR
from processing import load_tweets, load_user_sample
@@ -80,7 +83,7 @@ class Sample:
# date_pops[i] = Average popularity ratio of all posts from all users in this sample on date[i]
date_pops: list[float]
def __init__(self, name: str, users: list[str]):
def __init__(self, name: str, users: list[str]) -> None:
self.name = name
self.users = users
self.calculate_sample_data()
@@ -113,10 +116,10 @@ class Sample:
debug(f'Calculating sample tweets data for {self.name}...')
popularity = []
frequency = []
date_covid_count = dict()
date_all_count = dict()
self.user_all_pop_avg = dict()
self.user_date_covid_pop_avg = dict()
date_covid_count = {}
date_all_count = {}
self.user_all_pop_avg = {}
self.user_date_covid_pop_avg = {}
for i in range(len(self.users)):
u = self.users[i]
@@ -136,15 +139,14 @@ class Sample:
frequency.append(UserFloat(u, 0))
continue
# Calculate the frequency of COVID-related tweets
freq = len(covid) / len(tweets)
frequency.append(UserFloat(u, freq))
frequency.append(UserFloat(u, len(covid) / len(tweets)))
# Calculate date fields
# Assume tweets are sorted
# tweets.sort(key=lambda x: x.date)
# Calculate popularity by date
date_cp_sum = dict()
date_cp_count = dict()
date_cp_sum = {}
date_cp_count = {}
for t in tweets:
d = t.date[:10]
@@ -165,15 +167,15 @@ class Sample:
date_all_count[d] += 1
self.user_date_covid_pop_avg[u] = \
{d: date_cp_sum[d] / date_cp_count[d] for d in date_cp_sum}
{date: date_cp_sum[date] / date_cp_count[date] for date in date_cp_sum}
# Calculate total popularity ratio for a user
# To prevent divide by zero, ignore everyone who didn't post about covid
if len(covid) == 0:
continue
# Get the average popularity for COVID-related tweets
covid_pop_avg = sum(t.popularity for t in covid) / len(covid)
all_pop_avg = sum(t.popularity for t in tweets) / len(tweets)
covid_pop_avg = sum(tweet.popularity for tweet in covid) / len(covid)
all_pop_avg = sum(tweet.popularity for tweet in tweets) / len(tweets)
# Save global_avg
self.user_all_pop_avg[u] = all_pop_avg
# To prevent divide by zero, ignore everyone who literally have no likes on any post
@@ -183,7 +185,7 @@ class Sample:
popularity.append(UserFloat(u, covid_pop_avg / all_pop_avg))
# Calculate frequency on date
self.date_covid_freq = {d: date_covid_count[d] / date_all_count[d] for d in
self.date_covid_freq = {date: date_covid_count[date] / date_all_count[date] for date in
date_covid_count}
# Sort by relative popularity or frequency
@@ -220,8 +222,8 @@ class Sample:
self.dates.append(dt)
# Calculate date covid popularity ratio
users_posted_today = [u for u in self.users if u in self.user_date_covid_pop_avg and
ds in self.user_date_covid_pop_avg[u]]
users_posted_today = [u for u in self.users if u in self.user_date_covid_pop_avg
and ds in self.user_date_covid_pop_avg[u]]
if len(users_posted_today) == 0:
seven_days_user_prs.append([])
else:
@@ -230,6 +232,10 @@ class Sample:
seven_days_user_prs.append(user_prs)
# Average over seven days
# python_ta thinks user_prs is being shadowed here but it's not because the other
# instance is stuck in the else statement above
# python_ta also thinks that user_prs is possibly not defined here
# but it's in a comprehension so it is
seven_days_count = sum(len(user_prs) for user_prs in seven_days_user_prs)
if seven_days_count == 0:
pops_i = 1
@@ -301,10 +307,10 @@ def report_ignored(samples: list[Sample]) -> None:
"""
# For frequencies, report who didn't post
table = [["Total users"] + [str(len(s.users)) for s in samples],
["Users who didn't post at all"] +
[str(len([1 for a in s.user_freqs if a.data == 0])) for s in samples],
["Users who posted less than 1%"] +
[str(len([1 for a in s.user_freqs if a.data < 0.01])) for s in samples]]
["Users who didn't post at all"]
+ [str(len([1 for a in s.user_freqs if a.data == 0])) for s in samples],
["Users who posted less than 1%"]
+ [str(len([1 for a in s.user_freqs if a.data < 0.01])) for s in samples]]
Reporter('freq/didnt-post.md').table(table, [s.name for s in samples], True)
@@ -387,9 +393,7 @@ def graph_line_plot(x: list[datetime], y: Union[list[float], list[list[float]]],
"""
# Filter
if n > 0:
b = [1.0 / n] * n
a = 1
y = scipy.signal.lfilter(b, a, y)
y = scipy.signal.lfilter([1.0 / n] * n, 1, y)
border_color = '#5b3300'
@@ -436,8 +440,7 @@ def graph_line_plot(x: list[datetime], y: Union[list[float], list[list[float]]],
# Plotting frequency, add in the COVID cases data
if freq:
cases = get_covid_cases_us()
c = map_to_dates(cases.cases, [d.isoformat()[:10] for d in x])
c = map_to_dates(get_covid_cases_us(), [d.isoformat()[:10] for d in x])
c = filter_days_avg(c, 7)
c = scipy.signal.lfilter([1.0 / n] * n, 1, c)
@@ -512,13 +515,20 @@ def report_change_different_n(sample: Sample) -> None:
:param sample: Sample
:return: None
"""
for n in range(5, 16, 5):
for n in [5, 10, 15]:
graph_line_plot(sample.dates, sample.date_pops, f'change/n/{n}.png',
f'COVID-posting popularity ratio over time for {sample.name} IIR(n={n})',
False, n)
def report_change_graphs(sample: Sample) -> None:
"""
Report COVID-posting popularity ratio vs. time and COVID-posting frequency vs time,
both with IIR(10) filter
:param sample: Sample
:return: None
"""
graph_line_plot(sample.dates, sample.date_pops, f'change/pop/{sample.name}.png',
f'COVID-posting popularity ratio over time for {sample.name} IIR(10)',
False, 10)
@@ -530,7 +540,7 @@ def report_change_graphs(sample: Sample) -> None:
def report_all() -> None:
"""
Generate all reports
Preconditions:
- Twitter data have been downloaded and processed.
"""
@@ -553,9 +563,24 @@ def report_all() -> None:
report_change_graphs(s)
report_change_different_n(samples[0])
# python_ta thinks that s is shadowing again but the other instance is in the for loop above
# or in another comprehension so clearly there is no shadowing
graph_line_plot(samples[0].dates, [s.date_pops for s in samples], 'change/comb/pop.png',
'COVID-posting popularity ratio over time for all samples - IIR(10)', False, 10,
labels=[s.name for s in samples])
graph_line_plot(samples[0].dates, [s.date_freqs for s in samples], 'change/comb/freq.png',
'COVID-posting frequency over time for all samples - IIR(10)', True, 10,
labels=[s.name for s in samples])
if __name__ == '__main__':
# python_ta.contracts.check_all_contracts()
python_ta.check_all(config={
'extra-imports': ['os.path', 'dataclasses', 'datetime', 'pathlib', 'typing', 'matplotlib',
'matplotlib.dates', 'matplotlib.ticker', 'scipy.signal', 'collect_others',
'processing', 'constants', 'utils'
], # the names (strs) of imported modules
'allowed-io': ['report_all'], # the names (strs) of functions that call print/open/input
'max-line-length': 100,
'disable': ['R1705', 'C0200', 'E9988', 'E9969', 'R0902', 'R1702', 'R0913']
}, output='pyta_report.html')