Optimize imports.

[chrisu/seepark.git] / web / seepark_web.py

diff --git a/web/seepark_web.py b/web/seepark_web.py
index c6ceffb8614b6f049317983f6372bcc217e12048..a75acbfd603cbd717b128be547e8a1fb05937d29 100644 (file)
--- a/web/seepark_web.py
+++ b/web/seepark_web.py
@@ -1,23 +1,23 @@
-import collections
+import configparser
 import datetime
+import io
 import itertools
-import time
-import configparser
 import os
-import sys
 from collections import defaultdict
-import io
-import numpy as np
+
 import matplotlib
-matplotlib.use('pdf')
+import numpy as np
+
 import matplotlib.pyplot as plt
 from matplotlib.backends.backend_pdf import PdfPages
 
 from flask import Flask, render_template, jsonify, request, abort, Response, make_response
-import flask.json
-from flask_sqlalchemy import SQLAlchemy, inspect
+from flask_sqlalchemy import SQLAlchemy
 from sqlalchemy import func
 
+matplotlib.use('pdf')
+
+
 MONTH_DE = [
     'Jänner',
     'Februar',
@@ -42,21 +42,6 @@ DAY_OF_WEEK_DE = [
     'Sonntag']
 
 
-# https://stackoverflow.com/a/37350445
-def sqlalchemy_model_to_dict(model):
-    return {c.key: getattr(model, c.key)
-        for c in inspect(model).mapper.column_attrs}
-
-
-class JSONEncoder(flask.json.JSONEncoder):
-    def default(self, object):
-        if isinstance(object, datetime.datetime):
-            return object.isoformat()
-        elif isinstance(object, db.Model):
-            return sqlalchemy_model_to_dict(object)
-        return super().default(object)
-
-
 def parse_datetime(date_str):
     return datetime.datetime.strptime(date_str, '%Y-%m-%dT%H:%M:%S')
 
@@ -81,11 +66,11 @@ cityid = config.get('openweathermap', 'cityid')
 mainsensor = config.get('webapp', 'mainsensor')
 
 app = Flask(__name__)
-app.json_encoder = JSONEncoder
 app.config['SQLALCHEMY_DATABASE_URI'] = get_sqlalchemy_database_uri(config)
 app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False
 db = SQLAlchemy(app)
-db.reflect(app=app)
+with app.app_context():
+    db.reflect()
 
 
 class Sensors(db.Model):
@@ -192,32 +177,6 @@ def select_openweatherdata_grouped(cityid, begin, end):
     return query.all()
 
 
-def estimate_swimmer_count(date):
-    return date.day
-
-
-def select_swimmerdata(begin, end):
-    def report_times(begin, end):
-        d = begin
-        while d < end:
-            for t in [10, 15]:
-                a = datetime.datetime.combine(d.date(), datetime.time(t))
-                if a >= d:
-                    yield a
-            d += datetime.timedelta(days=1)
-    SwimmerData = collections.namedtuple('SwimmerData', ['datetime', 'count'])
-    for d in report_times(begin, end):
-        count = estimate_swimmer_count(d)
-        yield SwimmerData(d, count)
-
-
-def swimmerdata_to_xy(swimmerdata):
-    swimmerdata = list(swimmerdata)
-    x = np.array([d.datetime for d in swimmerdata])
-    y = np.array([d.count for d in swimmerdata])
-    return x, y
-
-
 def convert_to_c3(result, id, field_x, field_y):
     c3result = defaultdict(list)
     for row in result:
@@ -376,7 +335,6 @@ def report(year, month):
 
     water_data = sensordata_to_xy(select_sensordata(mainsensor, 'Wassertemperatur', begin, end))
     air_data = openweatherdata_to_xy(select_openweatherdata(cityid, begin, end))
-    swimmer_data = swimmerdata_to_xy(select_swimmerdata(begin, end))
 
     report_times = [datetime.time(10), datetime.time(15)]
     report_data = {'Wasser': water_data, 'Luft': air_data}
@@ -413,8 +371,6 @@ def report(year, month):
         for label in sorted(report_data.keys(), reverse=True):
             for t in report_times:
                 rows.append('{:02d}:{:02d} {} °C'.format(t.hour, t.minute, label))
-        for t in report_times:
-            rows.append('{:02d}:{:02d} Badende'.format(t.hour, t.minute))
         cells = []
         for label, data in sorted(report_data.items(), reverse=True):
             for t in report_times:
@@ -422,26 +378,17 @@ def report(year, month):
                 x, y = data
                 for d in days_datetime:
                     report_datetime = datetime.datetime.combine(d.date(), t)
-                    closest_index = np.argmin(np.abs(x - report_datetime))
-                    if abs(x[closest_index] - report_datetime) > datetime.timedelta(hours=1):
+                    if len(x) == 0:
                         cell = 'N/A'
                     else:
-                        value = y[closest_index]
-                        cell = '{:.1f}'.format(value)
+                        closest_index = np.argmin(np.abs(x - report_datetime))
+                        if abs(x[closest_index] - report_datetime) > datetime.timedelta(hours=1):
+                            cell = 'N/A'
+                        else:
+                            value = y[closest_index]
+                            cell = '{:.1f}'.format(value)
                     row_cells.append(cell)
                 cells.append(row_cells)
-        for t in report_times:
-            row_cells = []
-            x, y = swimmer_data
-            for d in days_datetime:
-                report_datetime = datetime.datetime.combine(d.date(), t)
-                closest_index = np.argmin(np.abs(x - report_datetime))
-                if abs(x[closest_index] - report_datetime) > datetime.timedelta(hours=1):
-                    cell = 'N/A'
-                else:
-                    cell = y[closest_index]
-                row_cells.append(cell)
-            cells.append(row_cells)
         row_colors = list(ntimes(report_colors + ['w'], len(report_times)))
         table = plt.table(cellText=cells, colLabels=columns, rowLabels=rows, rowColours=row_colors, loc='bottom')
         table.scale(xscale=1, yscale=2)