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Merged
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Apr 18, 2019
Merged

refactoring #28 #32

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Binary file modified __pycache__/config.cpython-36.pyc
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271 changes: 148 additions & 123 deletions app/analytics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,7 @@
import altair as alt
from altair import expr, datum
import pandas as pd
import datetime as datetime
from config import conn

@cache.cached(timeout=60*60, key_prefix='hourly_db')
Expand All @@ -17,120 +18,52 @@ def getdb():
conn, index_col='id')

df = getdb()
# convert datetime to '2018-09' month and year format
# df['mnth_yr'] = df['workshop_start'].dt.to_period('M').astype(str)
# df['workshop_category'] = df['workshop_category'].astype('category')

@app.before_request
def before_request():
def getuserdb():
if current_user.is_authenticated:
employee = Employee.query.filter_by(email=current_user.email).first()
if employee is not None:
df['this_user'] = df['workshop_instructor'] == employee.id
g.user_melted = pd.melt(
df[df['this_user'] == True],
id_vars=['mnth_yr', 'workshop_category'],
value_vars=['workshop_hours', 'class_size'])

g.df3 = df[df['this_user'] == True].copy()
g.df3['workshop_category'] = pd.Categorical(g.df3['workshop_category']).codes
g.dat2 = g.df3.set_index('workshop_start').resample('W').sum()
g.accum_personal = pd.melt(g.dat2.reset_index(),
id_vars=['workshop_start','workshop_category'],
value_vars=['workshop_hours', 'class_size'])
g.accum_personal['workshop_category'] = g.accum_personal['workshop_category'].apply(lambda x: 'Corporate' if (x == 1) else 'Public')
g.accum_personal['cumsum'] = g.accum_personal.groupby(['variable','workshop_category']).cumsum().fillna(0)

g.df2 = df.copy()
g.df2['mnth_yr'] = g.df2['workshop_start'].dt.to_period('M').astype(str)
g.df2['workshop_category'] = pd.Categorical(g.df2['workshop_category']).codes
g.df2['workshop_category'] = g.df2['workshop_category'].astype('category')

g.dat = df[['workshop_start','workshop_category', 'workshop_hours', 'class_size']].copy()
g.dat['workshop_category'] = pd.Categorical(g.dat['workshop_category']).codes
g.dat['workshop_category'] = g.dat['workshop_category'].astype('category')

g.dat['workshop_category'] = g.dat['workshop_category'].apply(lambda x: 'Corporate' if (x == 1) else 'Public')
g.dat = g.dat.set_index(
'workshop_start').groupby(
'workshop_category').resample('W').sum()
g.dat.sort_index(inplace=True)

g.accum = pd.melt(g.dat.reset_index(),
id_vars=['workshop_start','workshop_category'],
value_vars=['workshop_hours', 'class_size'])
g.accum['cumsum'] = g.accum.groupby(['variable','workshop_category']).cumsum()
g.accum = g.accum.sort_values(['workshop_category', 'workshop_start'])

g.accumtotal = df[['workshop_start', 'class_size']].copy().set_index('workshop_start').sort_index().reset_index()
g.accumtotal['cumsum'] = g.accumtotal['class_size'].cumsum()

@app.route('/data/class_size_vs')
@cache.cached(timeout=86400, key_prefix='cs_vs')
def class_size_vs():
brush = alt.selection(type='interval', encodings=['x'])
upper = alt.Chart(df[df['this_user'] == True]).mark_area(
clip=True,
opacity=0.75,
interpolate='monotone'
).encode(
x=alt.X("mnth_yr:T", axis=alt.Axis(title=''), scale={'domain':brush.ref()}),
y=alt.Y('sum(workshop_hours)', axis=alt.Axis(title='Workshop Hours')),
color=alt.Color(
'workshop_category',
scale=alt.Scale(range=['#1a1d21', '#7dbbd2cc', '#153f5a', '#bbc6cbe6']),
#'#7dbbd2cc', '#bbc6cbe6'
legend=None
),
tooltip=['workshop_category']
).properties(width=450)
lower = alt.Chart(df[df['this_user'] == True]).mark_rect(color='#75b3cacc').encode(
x=alt.X("mnth_yr:T", axis=alt.Axis(title='Interval Selector'), scale={'domain':brush.ref()})
).add_selection(
brush
).properties(width=450)
chart = alt.vconcat(upper, lower, data=df[df['this_user'] == True]).configure_axis(
labelColor='#bbc6cbe6',
titleColor='#bbc6cbe6',
grid=False)

return chart.to_json()

@app.route('/data/class_size_hours')
@cache.cached(timeout=86400, key_prefix='cs_hours')
def class_size_hours():
chart = alt.Chart(g.user_melted).mark_bar().encode(
column='variable',
x=alt.X("sum(value)"),
y=alt.Y('mnth_yr'),
color=alt.Color(
'workshop_category',
scale=alt.Scale(range=['#7dbbd2cc', '#bbc6cbe6', '#6eb0ea', '#d1d8e2', '#1a1d21', '#8f9fb3' ]),legend=None),
tooltip=['workshop_category', 'sum(value)']
).configure_axis(
grid=False
).properties(
width=250
)

return chart.to_json()
return df

# ================ ================ ================
# ================ Global Section ================
#
# Visualization using the overall population (global)
#
#
# ===================================================
# ===================================================

@app.route('/data/accum_global')
@cache.cached(timeout=86400, key_prefix='accum_g')
def accum_global():
chart = alt.Chart(g.accum).mark_area().encode(
dat = df.copy()
dat = dat.append({'workshop_start': datetime.datetime.now(), 'workshop_category': 'Corporate'}, ignore_index=True)
dat = dat.append({'workshop_start': datetime.datetime.now(), 'workshop_category': 'Academy'}, ignore_index=True)
dat['workshop_category'] = dat['workshop_category'].apply(lambda x: 'Corporate' if (x == 'Corporate') else 'Public').astype('category')
dat = dat.loc[:,['workshop_start', 'workshop_category', 'workshop_hours', 'class_size']]\
.set_index('workshop_start')\
.groupby('workshop_category')\
.resample('W').sum().reset_index()

dat['workshop hours']=dat.groupby(['workshop_category'])['workshop_hours'].cumsum()
dat['students']=dat.groupby(['workshop_category'])['class_size'].cumsum()
dat = dat.melt(id_vars=['workshop_start', 'workshop_category'],value_vars=['workshop hours', 'students'])

chart = alt.Chart(dat).mark_area().encode(
column=alt.Column('workshop_category', title=None, sort="descending",
header=alt.Header(
labelColor='#ffffff',
titleAnchor="start")),
x=alt.X("workshop_start", title="Date"),
y=alt.Y("cumsum:Q", title="Cumulative"),
y=alt.Y("value:Q", title="Cumulative"),
color=alt.Color("variable",
scale=alt.Scale(
range=['#7dbbd2cc', '#bbc6cbe6']),
legend=None
),
tooltip=['variable', 'cumsum:Q']
tooltip=['variable', 'value:Q']
).properties(width=350).configure_axis(
labelColor='#bbc6cbe6',
titleColor='#bbc6cbe6',
Expand All @@ -143,6 +76,10 @@ def accum_global():
@app.route('/data/accum_global_line')
@cache.cached(timeout=86400, key_prefix='gt_line')
def accum_global_line():
dat = df.copy()
dat = dat[['workshop_start', 'class_size']].sort_values(by='workshop_start')
dat['cumsum'] = dat['class_size'].cumsum()

brush = alt.selection(type='interval', encodings=['x'])
# Create a selection that chooses the nearest point & selects based on x-value
nearest = alt.selection(type='single', nearest=True, on='mouseover',
Expand All @@ -151,7 +88,7 @@ def accum_global_line():
x=alt.X("workshop_start:T", axis=alt.Axis(title='', grid=False),scale={'domain': brush.ref()}),
y=alt.Y("cumsum", axis=alt.Axis(title='Total Students', grid=False))
)
selectors = alt.Chart(g.accumtotal).mark_point().encode(
selectors = alt.Chart(dat).mark_point().encode(
x=alt.X("workshop_start:T"),
opacity=alt.value(0)
).add_selection(
Expand All @@ -169,7 +106,7 @@ def accum_global_line():
nearest
)

upper = alt.layer(line, selectors, points, rules, text, data=g.accumtotal, width=350)
upper = alt.layer(line, selectors, points, rules, text, data=dat, width=350)
lower = alt.Chart().mark_area(color='#75b3cacc').encode(
x=alt.X("workshop_start:T", axis=alt.Axis(title=''), scale={
'domain':brush.ref()
Expand All @@ -182,34 +119,20 @@ def accum_global_line():
brush
)

chart = alt.vconcat(upper,lower, data=g.accumtotal).configure_view(
chart = alt.vconcat(upper,lower, data=dat).configure_view(
strokeWidth=0
)
return chart.to_json()

@app.route('/data/accum_personal')
def accum_personal():
chart = alt.Chart(g.accum_personal).mark_area().encode(
column='workshop_category',
x=alt.X("workshop_start"),
y=alt.Y("cumsum:Q"),
color=alt.Color("variable",
scale=alt.Scale(
range=['#7dbbd2cc', '#bbc6cbe6']),
legend=None
),
tooltip=['variable', 'cumsum:Q']
).properties(
width=250
)
return chart.to_json()

@app.route('/data/punchcode')
@cache.cached(timeout=86400, key_prefix='gt_line')
def punchcode():
g.df2['workshop_category'] = g.df2['workshop_category'].apply(lambda x:'Corporate' if x == 1 else 'Public' )
g.df2['contrib'] = g.df2['workshop_hours'] * g.df2['class_size']
dat = df.copy()
dat['mnth_yr'] = dat['workshop_start'].dt.to_period('M').astype(str)
dat['workshop_category'] = dat['workshop_category'].apply(lambda x: 'Corporate' if (x == 'Corporate') else 'Public')
dat['contrib'] = dat['workshop_hours'] * dat['class_size']

chart = alt.Chart(g.df2[g.df2.name != 'Capstone']).mark_circle(color='#bbc6cbe6').encode(
chart = alt.Chart(dat[dat.name != 'Capstone']).mark_circle(color='#bbc6cbe6').encode(
x=alt.X('mnth_yr:T', axis=alt.Axis(title='')),
y='name:O',
size=alt.Size('sum(contrib):Q', legend=None),
Expand All @@ -223,6 +146,7 @@ def punchcode():
return chart.to_json()

@app.route('/data/category_bars')
@cache.cached(timeout=86400, key_prefix='gt_line')
def category_bars():
chart = alt.Chart(df).mark_bar(color='#bbc6cbe6').encode(
x=alt.X('sum(workshop_hours):Q', title='Accumulated Hours'),
Expand All @@ -231,6 +155,103 @@ def category_bars():
)
return chart.to_json()


# ================ ================ ================
# ================ Person Section ================
#
# Visualization relating to individual instructor
#
#
# ===================================================
# ===================================================

@app.route('/data/person_contrib_area')
@cache.cached(timeout=86400, key_prefix='p_contrb')
def person_contrib_area():
dat_ori = getuserdb()
dat = dat_ori.loc[dat_ori.this_user == True,:].copy()
dat['contrib'] = dat['workshop_hours'] * dat['class_size']

brush = alt.selection(type='interval', encodings=['x'])
upper = alt.Chart(dat).mark_area(
clip=True,
color='#7c98ae',
opacity=1,
interpolate='monotone'
).encode(
x=alt.X("workshop_start:T", axis=alt.Axis(title=''), scale={'domain':brush.ref()}),
y=alt.Y('sum(contrib)', axis=alt.Axis(title='Activities'))
).properties(width=450)
lower = alt.Chart(dat).mark_rect(color='#75b3cacc').encode(
x=alt.X("workshop_start:T", axis=alt.Axis(title='Interval Selector'), scale={'domain':brush.ref()})
).add_selection(
brush
).properties(width=450)
chart = alt.vconcat(upper, lower, data=dat).configure_axis(
labelColor='#bbc6cbe6',
titleColor='#bbc6cbe6',
grid=False)

return chart.to_json()

@app.route('/data/person_class_bar')
@cache.cached(timeout=86400, key_prefix='p_hours')
def person_class_bar():
dat_ori = getuserdb()
dat = dat_ori.loc[dat_ori.this_user == True,:].copy()
dat['mnth_yr'] = dat['workshop_start'].dt.to_period('M').astype(str)
dat = dat.melt(
id_vars=['mnth_yr', 'workshop_category'],
value_vars=['workshop_hours', 'class_size'])
chart = alt.Chart(dat).mark_bar().encode(
column='variable',
x=alt.X("sum(value)"),
y=alt.Y('mnth_yr'),
color=alt.Color(
'workshop_category',
scale=alt.Scale(range=['#7dbbd2cc', '#bbc6cbe6', '#6eb0ea', '#d1d8e2', '#1a1d21', '#8f9fb3' ]),legend=None),
tooltip=['workshop_category', 'sum(value)']
).configure_axis(
grid=False
).properties(
width=250
)

return chart.to_json()

@app.route('/data/person_vs_area')
def person_vs_area():
dat_ori = getuserdb()
dat = dat_ori.loc[dat_ori.this_user == True,:].copy()
dat = dat.append({'workshop_start': datetime.datetime.now(), 'workshop_category': 'Corporate'}, ignore_index=True)
dat = dat.append({'workshop_start': datetime.datetime.now(), 'workshop_category': 'Academy'}, ignore_index=True)
dat['workshop_category'] = dat['workshop_category'].apply(lambda x: 'Corporate' if (x == 'Corporate') else 'Public').astype('category')
dat = dat.loc[:,['workshop_start', 'workshop_category', 'workshop_hours', 'class_size']]\
.set_index('workshop_start')\
.groupby('workshop_category')\
.resample('W').sum().reset_index()
dat['workshop hours']=dat.groupby(['workshop_category'])['workshop_hours'].cumsum()
dat['students']=dat.groupby(['workshop_category'])['class_size'].cumsum()
dat = dat.melt(id_vars=['workshop_start', 'workshop_category'],value_vars=['workshop hours', 'students'])

chart = alt.Chart(dat).mark_area().encode(
column='workshop_category',
x=alt.X("workshop_start"),
y=alt.Y("cumsum:Q"),
color=alt.Color("variable",
scale=alt.Scale(
range=['#7dbbd2cc', '#bbc6cbe6']),
legend=None
),
tooltip=['variable', 'cumsum:Q']
).properties(
width=250
).configure_axis(
grid=False
)
return chart.to_json()


@app.route('/data/instructor_breakdown')
@cache.cached(timeout=86400, key_prefix='ib')
def instructor_breakdown():
Expand Down Expand Up @@ -324,9 +345,13 @@ def instructor_breakdown():
# )
# return chart.to_json()


# ================ ================ ================
# ================ Non-Chart Section ================
# Return Stats, usually in the form of Dictionary
#
# Each factory is responsible for the data required
# to render the chart and view for each page
#
# ===================================================
# ===================================================
@cache.cached(timeout=43200, key_prefix='gt_stats')
def factory_homepage():
Expand All @@ -346,12 +371,12 @@ def factory_homepage():
.rename(
columns={'workshop_hours':'Total Hours',
'class_size':'Total Students'})
.to_html(classes=['table thead-light table-striped table-bordered table-hover table-sm'], )
.to_html(classes=['table thead-light table-striped table-bordered table-hover table-sm'])
}
return stats


def person_total_stats(u):
def factory_accomplishment(u):
workshops = Workshop.query.filter_by(
workshop_instructor=u.id).order_by(Workshop.workshop_start.desc())
grped = dict()
Expand Down Expand Up @@ -388,7 +413,7 @@ def person_total_stats(u):
'fullstar': fullstar,
'responsecount': len(responses),
'qualitative': qualitative,
'topten': g.df2[g.df2.name != 'Capstone'].loc[:,['name','workshop_hours', 'class_size']].groupby(
'topten': df[df.name != 'Capstone'].loc[:,['name','workshop_hours', 'class_size']].groupby(
'name').sum().sort_values(
by='workshop_hours',
ascending=False).head(10).rename_axis(None).to_html(classes=['table thead-light table-striped table-bordered table-hover table-sm'])
Expand Down
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