cleanup old stuff, move test to __init__

emfit/__init__.py

@@ -282,3 +282,8 @@ def by_night() -> Dict[date, Emfit]:
         res[s.date] = s
     return res
 
+
+
+def test():
+    for d in get_datas():
+        assert len(d.epochs) > 0

plot.py

@@ -2,49 +2,10 @@
 import matplotlib.dates as md # type: ignore
 import numpy as np # type: ignore
 import seaborn as sns # type: ignore
-import matplotlib.pyplot as plt
+import matplotlib.pyplot as plt # type: ignore
 
 from emfit import get_datas
 
-
-def plot_file(jj: str):
-    pts = jj['sleep_epoch_datapoints']
-
-
-    tss = [datetime.fromtimestamp(p[0]) for p in pts]
-    vals = [p[1] for p in pts]
-
-    plt.figure(figsize=(20,10))
-    plt.plot(tss, vals)
-
-
-    xformatter = md.DateFormatter('%H:%M')
-    xlocator = md.MinuteLocator(interval = 15)
-
-    ## Set xtick labels to appear every 15 minutes
-    plt.gcf().axes[0].xaxis.set_major_locator(xlocator)
-
-    ## Format xtick labels as HH:MM
-    plt.gcf().axes[0].xaxis.set_major_formatter(xformatter)
-
-
-
-    plt.xlabel('time')
-    plt.ylabel('phase')
-    plt.title('Sleep phases')
-    plt.grid(True)
-    plt.savefig(f"{f}.png")
-    plt.close() # TODO
-    # plt.show()
-
-    pass
-
-
-def plot_all():
-    for jj in iter_datas():
-        plot_file(jj)
-
-
 # def stats():
 #     for jj in iter_datas():
 #         # TODO fimezone??
@@ -63,47 +24,6 @@ def plot_all():
 #         print(*res)
 
 
-def stats():
-    datas = get_datas()
-    cur = datas[0].date
-    for jj in datas:
-        # import ipdb; ipdb.set_trace() 
-        while cur < jj.date:
-            cur += timedelta(days=1)
-            if cur.weekday() == 0:
-                print("---")
-            if cur != jj.date:
-                print(" ")
-        # cur = jj.date
-        print(f"{jj.date.strftime('%m.%d %a')} {jj.hrv_morning:.0f} {jj.hrv_evening:.0f} {jj.hrv_morning - jj.hrv_evening:3.0f} {hhmm(jj.sleep_minutes)} {jj.hrv_lf}/{jj.hrv_hf} {jj.sleep_hr_coverage:3.0f}")
-
-
-def plot_recovery_vs_hr_percentage():
-    sns.set(color_codes=True)
-    xs = []
-    ys = []
-    for jj in get_datas():
-        xs.append(jj.hrv_morning - jj.hrv_evening)
-        ys.append(jj.sleep_hr_coverage)
-    ax = sns.regplot(x=xs, y=ys) # "recovery", y="percentage", data=pdata)
-    ax.set(xlabel='recovery', ylabel='percentage')
-    plt.show()
-
-
-# TODO ah. it's only last segment?
-# ok, handled in dashboard now
-def plot_hr():
-    jj = get_datas()[-1]
-    tss, uu = jj.sleep_hr
-    tss = tss[::10]
-    uu = uu[::10]
-    plt.figure(figsize=(15,4))
-    ax = sns.pointplot(tss, uu, markers=" ")
-    # TODO wtf is that/??
-    ax.set(ylim=(None, 200))
-
-    plt.show()
-
 def plot_hr_trend():
     everything = get_datas()
     tss = [e.end for e in everything]
@@ -119,28 +39,13 @@ def plot_hr_trend():
 # also timeline should have dynamic filters? maybe by tags
 # then I could enable emfit feed and slog feed (pulled from all org notes) and see the correlation? also could pull workouts provider (and wlog) -- actually wlog processing could be moved to timeline too
 
-# TODO could plot 'recovery' thing and see if it is impacted by workouts
-
-
-# TODO time_start, time_end
-
-# plot_hrv()
-# stats()
-# plot_recovery_vs_hr_percentage()
-# stats()
 plot_hr_trend()
-# import matplotlib
-# matplotlib.use('Agg')
 
 
 # TODO maybe rmssd should only be computed if we have a reasonable chunk of datas
-# also, trust it only if it's stable
 
 # plot_timestamped([p[0] for p in pts], [p[1] for p in pts], mavgs=[]).savefig('res.png')
 # TODO X axes: show hours and only two dates
 # TODO 4 is awake, 3 REM, 2 light, 1 deep
 
-
-
-
 # deviartion beyond 25-75 or 75-25 is bad??

test.py

@@ -1,5 +0,0 @@
-#!/usr/bin/env python3
-from emfit import get_datas
-
-for d in get_datas():
-    print(d)