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Rebase origin/dev onto upstream/dev
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@christinaexyou @JamesKunstle
christinaexyou authored and JamesKunstle committed Oct 6, 2023
1 parent 496f1e9 commit ceacf0f
Showing 1 changed file with 81 additions and 46 deletions.
127 changes: 81 additions & 46 deletions 8Knot/pages/contributors/visualizations/contrib_importance_over_time.py
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Original file line number Diff line number Diff line change
Expand Up @@ -296,6 +296,8 @@ def process_data(df, patterns, threshold, window_width, step_size, start_date, e
(
df_final["Commit"],
df_final["Issue Opened"],
df_final["Issue Comment"],
df_final["Issue Closed"],
df_final["PR Opened"],
df_final["PR Comment"],
df_final["PR Review"],
Expand All @@ -309,49 +311,82 @@ def process_data(df, patterns, threshold, window_width, step_size, start_date, e


def create_figure(df_final, step_size):
# create custom data to update the hovertemplate with the action type and start and end dates of a given time window
action_types = [[action_type] * len(df_final) for action_type in df_final.columns[2:]]
time_window = list(df_final["period_from"].dt.strftime("%b %d, %Y") + " - " + df_final["period_to"].dt.strftime("%b %d, %Y"))

# create plotly express line graph
fig = go.Figure(
[
go.Scatter(
name="Commit",
x=df_final["period_from"],
y=df_final["Commit"],
mode="lines",
showlegend=True,
marker=dict(color=color_seq[0]),
),
go.Scatter(
name="Issue Opened",
x=df_final["period_from"],
y=df_final["Issue Opened"],
mode="lines",
showlegend=True,
marker=dict(color=color_seq[1]),
),
go.Scatter(
name="PR Opened",
x=df_final["period_from"],
y=df_final["PR Opened"],
mode="lines",
showlegend=True,
marker=dict(color=color_seq[2]),
),
go.Scatter(
name="PR Request Comment",
x=df_final["period_from"],
y=df_final["PR Comment"],
mode="lines",
showlegend=True,
marker=dict(color=color_seq[3]),
),
go.Scatter(
name="PR Request Review",
x=df_final["period_from"],
y=df_final["PR Review"],
mode="lines",
showlegend=True,
marker=dict(color=color_seq[4]),
),
go.Scatter(name="Commit",
x=df_final["period_from"],
y = df_final["Commit"],
text=action_types[0],
customdata=time_window,
mode="lines",
showlegend=True,
marker=dict(color=color_seq[0])
),

go.Scatter(name="Issue Opened",
x=df_final["period_from"],
y=df_final["Issue Opened"],
text=action_types[1],
customdata=time_window,
mode="lines",
showlegend=True,
marker=dict(color=color_seq[1])
),

go.Scatter(name="Issue Comment",
x=df_final["period_from"],
y=df_final["Issue Comment"],
text=action_types[2],
customdata=time_window,
mode="lines",
showlegend=True,
marker=dict(color=color_seq[2])
),

go.Scatter(name="Issue Closed",
x=df_final["period_from"],
y=df_final["Issue Closed"],
text=action_types[3],
customdata=time_window,
mode="lines",
showlegend=True,
marker=dict(color=color_seq[3])
),

go.Scatter(name="PR Opened",
x=df_final["period_from"],
y=df_final["PR Opened"],
text=action_types[4],
customdata=time_window,
mode="lines",
showlegend=True,
marker=dict(color=color_seq[4])
),

go.Scatter(name="PR Comment",
x=df_final["period_from"],
y=df_final["PR Comment"],
text=action_types[5],
customdata=time_window,
mode="lines",
showlegend=True,
marker=dict(color=color_seq[5])
),

go.Scatter(name="PR Review",
x=df_final["period_from"],
y=df_final["PR Review"],
text=action_types[6],
customdata=time_window,
mode="lines",
showlegend=True,
marker=dict(color=color_seq[0])
)
],
)

Expand All @@ -370,7 +405,7 @@ def create_figure(df_final, step_size):
# hover template styling
fig.update_traces(
textposition="top right",
hovertemplate="Date: %{x}" + "<br>Contributor prolificacy: %{y}<br><extra></extra>",
hovertemplate="%{text}" + "<br>Date: %{customdata}" + "<br>Contributor Prolificacy: %{y}<br><extra></extra>",
)

# layout syling
Expand All @@ -385,14 +420,13 @@ def create_figure(df_final, step_size):

return fig


def cntrb_prolificacy_over_time(df, period_from, period_to, window_width, threshold):
# subset df such that the rows correspond to the window of time defined by period from and period to
time_mask = (df["created_at"] >= period_from) & (df["created_at"] <= period_to)
df_in_range = df.loc[time_mask]

# initialize varibles to store contributor prolificacy accoding to action type
commit, issueOpened, prOpened, prReview, prComment = None, None, None, None, None
commit, issueOpened, issueComment, issueClosed, prOpened, prReview, prComment = None, None, None, None, None, None, None

# count the number of contributions each contributor has made according each action type
df_count_cntrbs = df_in_range.groupby(["Action", "cntrb_id"])["cntrb_id"].count().to_frame()
Expand All @@ -403,12 +437,13 @@ def cntrb_prolificacy_over_time(df, period_from, period_to, window_width, thresh

commit = calc_cntrb_prolificacy(df_count_cntrbs, "Commit", threshold)
issueOpened = calc_cntrb_prolificacy(df_count_cntrbs, "Issue Opened", threshold)
issueComment = calc_cntrb_prolificacy(df_count_cntrbs, "Issue Comment", threshold)
issueClosed = calc_cntrb_prolificacy(df_count_cntrbs, "Issue Closed", threshold)
prOpened = calc_cntrb_prolificacy(df_count_cntrbs, "PR Opened", threshold)
prReview = calc_cntrb_prolificacy(df_count_cntrbs, "PR Review", threshold)
prComment = calc_cntrb_prolificacy(df_count_cntrbs, "PR Comment", threshold)

return commit, issueOpened, prOpened, prReview, prComment

return commit, issueOpened, issueComment, issueClosed, prOpened, prReview, prComment

def calc_cntrb_prolificacy(df, action_type, threshold):
# if the df is empty return None
Expand Down Expand Up @@ -442,4 +477,4 @@ def calc_cntrb_prolificacy(df, action_type, threshold):
if running_sum >= thresh_cntrbs:
break

return cntrb_prolificacy
return cntrb_prolificacy

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