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[+] Create graph_line_plot()

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Hykilpikonna
2021-11-25 19:04:07 -05:00
parent 2f16fe2162
commit 3958497bff
2 changed files with 71 additions and 26 deletions
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.idea/inspectionProfiles/Project_Default.xml
Generated
+7
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@@ -33,5 +33,12 @@
<option name="myAdditionalJavadocTags" value="date" />
</inspection_tool>
<inspection_tool class="JpaDataSourceORMInspection" enabled="false" level="ERROR" enabled_by_default="false" />
<inspection_tool class="PyUnresolvedReferencesInspection" enabled="true" level="WARNING" enabled_by_default="true">
<option name="ignoredIdentifiers">
<list>
<option value="bins" />
</list>
</option>
</inspection_tool>
</profile>
</component>
src/process/twitter_visualization.py
+64 -26
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@@ -126,7 +126,7 @@ class Sample:
ignores users, but instead combines the tweets of the entire sample in the calculation.
More details about the calculations can be found in the report, or report_document.md
Preconditions:
- len(self.tweets) > 0
- self.tweets != None
@@ -167,23 +167,7 @@ class Sample:
# Convert indicies to dates, which will be our x-axis
first_date = parse_date(self.tweets[0].date).replace(hour=0, minute=0, second=0)
dates = [first_date + timedelta(days=j) for j in range(len(all_count))]
# Find suitable n
for n in range(1, 20, 3):
# Reduce noise by averaging results over 7 day frame
b = [1.0 / n] * n
a = 1
f = scipy.signal.lfilter(b, a, self.date_freqs)
p = scipy.signal.lfilter(b, a, self.date_pops)
# plt.title(f'COVID-posting frequency over time for {sample.name} with IIR n = {n}')
# plt.plot(dates, f)
# plt.show()
plt.title(f'COVID-posting popularity ratio over time for {self.name} with IIR n = {n}')
plt.plot(dates, p)
plt.savefig(f'{REPORT_DIR}/test/{n}.png')
plt.clf()
self.dates = [first_date + timedelta(days=j) for j in range(len(all_count))]
def load_samples() -> list[Sample]:
@@ -241,7 +225,7 @@ def report_ignored(samples: list[Sample]) -> None:
Reporter('pop/ignored.md').table(table, [s.name for s in samples], True)
def load_font() -> None:
def graph_load_font() -> None:
"""
Load iosevka font for matplotlib
"""
@@ -251,8 +235,8 @@ def load_font() -> None:
plt.rcParams["font.family"] = "iosevka"
def report_histogram(x: list[float], path: str, title: str, clear_outliers: bool = False,
bins: int = 20, axvline: Union[list[int], None] = None) -> None:
def graph_histogram(x: list[float], path: str, title: str, clear_outliers: bool = False,
bins: int = 20, axvline: Union[list[int], None] = None) -> None:
"""
Plot a histogram
@@ -294,6 +278,47 @@ def report_histogram(x: list[float], path: str, title: str, clear_outliers: bool
fig.savefig(os.path.join(REPORT_DIR, path))
def graph_line_plot(x: Union[list[float], list[datetime]], y: list[float], path: str, title: str,
n: int = 0) -> None:
"""
Plot a line plot, and reduce noise using an IIR filter
:param x: X axis data
:param y: Y axis data
:param n: IIR filter parameter (Ignored if n <= 0)
:param path: Output image path (should end in .png)
:param title: Title
:return: None
"""
# Filter
if n > 0:
b = [1.0 / n] * n
a = 1
y = scipy.signal.lfilter(b, a, y)
border_color = '#5b3300'
# Create fig ax
fig: plt.Figure
ax: plt.Axes
fig, ax = plt.subplots()
ax.margins(x=0, y=0)
# Plot
ax.set_title(title, color=border_color)
ax.plot(x, y, color='#d4b595')
# Colors
ax.tick_params(color=border_color, labelcolor=border_color)
for spine in ax.spines.values():
spine.set_edgecolor(border_color)
# Save
path = Path(os.path.join(REPORT_DIR, path))
path.parent.mkdir(parents=True, exist_ok=True)
fig.savefig(str(path))
def report_histograms(sample: Sample) -> None:
"""
Report histograms of COVID posting frequencies and popularity ratios
@@ -303,14 +328,14 @@ def report_histograms(sample: Sample) -> None:
"""
x = [f.data for f in sample.user_freqs]
title = f'COVID-related posting frequency for {sample.name}'
report_histogram(x, f'freq/{sample.name}-hist-outliers.png', title, False, 100)
graph_histogram(x, f'freq/{sample.name}-hist-outliers.png', title, False, 100)
x = [p for p in x if p > 0.001]
report_histogram(x, f'freq/{sample.name}-hist.png', title, True)
graph_histogram(x, f'freq/{sample.name}-hist.png', title, True)
x = [f.data for f in sample.user_pops]
title = f'Popularity ratio of COVID posts for {sample.name}'
report_histogram(x, f'pop/{sample.name}-hist-outliers.png', title, False, 100, axvline=[1])
report_histogram(x, f'pop/{sample.name}-hist.png', title, True, axvline=[1])
graph_histogram(x, f'pop/{sample.name}-hist-outliers.png', title, False, 100, axvline=[1])
graph_histogram(x, f'pop/{sample.name}-hist.png', title, True, axvline=[1])
def report_stats(samples: list[Sample]) -> None:
@@ -343,11 +368,23 @@ def view_covid_tweets_date(tweets: list[Posting]):
plt.show()
def report_change_different_n(sample: Sample) -> None:
"""
Experiment wth different n values for IIR filter
:param sample: Sample
:return: None
"""
for n in range(1, 15, 3):
graph_line_plot(sample.dates, sample.date_pops, f'{REPORT_DIR}/change/n/{n}.png',
f'COVID-posting popularity ratio over time for {sample.name} IIR(n={n})', n)
def report_all() -> None:
"""
Generate all reports
"""
load_font()
graph_load_font()
Path(f'{REPORT_DIR}/freq').mkdir(parents=True, exist_ok=True)
Path(f'{REPORT_DIR}/pop').mkdir(parents=True, exist_ok=True)
@@ -363,6 +400,7 @@ def report_all() -> None:
for s in samples:
report_top_20_tables(s)
report_histograms(s)
report_change_different_n(samples[0])
if __name__ == '__main__':