diff --git a/.idea/inspectionProfiles/Project_Default.xml b/.idea/inspectionProfiles/Project_Default.xml
index f89a480..4e56ad3 100644
--- a/.idea/inspectionProfiles/Project_Default.xml
+++ b/.idea/inspectionProfiles/Project_Default.xml
@@ -33,5 +33,12 @@
+
+
+
\ No newline at end of file
diff --git a/src/process/twitter_visualization.py b/src/process/twitter_visualization.py
index 8149911..5e04399 100644
--- a/src/process/twitter_visualization.py
+++ b/src/process/twitter_visualization.py
@@ -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__':