Merge pull request #30 from Mixss/quickfixes

Generate advanced forecasts with real data and fix bot-breaking bugs

Author: pcichowski

.gitignore

@@ -2,4 +2,5 @@ env/
 __pycache__/
 venv/
 .idea/
-logo.png
+logo.png
+.venv/

data/config.json

@@ -1 +1 @@
-{"BroadcastChannels": [{"ServerId": 465940323365814272, "ChannelId": 828951713074380840}, {"ServerId": 913375693864308797, "ChannelId": 958682054126612480}]}
+{"BroadcastChannels": [{"ServerId": 465940323365814272, "ChannelId": 828951713074380840}, {"ServerId": 913375693864308797, "ChannelId": 958119472793780304}]}

logic/advanced_forecast.py

@@ -4,6 +4,8 @@
 import matplotlib as mpl
 from scipy.interpolate import make_interp_spline, BSpline
 
+from logic.logic import get_advanced_hourly_forecast, get_day_of_week_capitalized, get_today
+
 INTERPOLATION_COEFFICIENT = 20
 
 mpl.rcParams['axes.labelsize'] = 18
@@ -16,84 +18,85 @@ def interpolate_line(x, y, k_coeff):
     x_arr = np.array(x).astype(float)
     y_arr = np.array(y).astype(float)
 
-    new_x = np.linspace(x_arr.min(), x_arr.max(), INTERPOLATION_COEFFICIENT * x_arr.size)
+    x_range = np.arange(len(x))
+
+    spline = make_interp_spline(x_range, y_arr, k=k_coeff)
+
+    x_smooth = np.linspace(0, len(x_arr) - 1, INTERPOLATION_COEFFICIENT * x_arr.size)
 
-    spline = make_interp_spline(x_arr, y_arr, k=k_coeff)
-    y_smooth = spline(new_x)
+    y_smooth = spline(x_smooth)
 
-    return new_x, y_smooth
+    interpolated_x_in_x = np.interp(x_smooth, x_range, x_arr)
 
+    return interpolated_x_in_x, y_smooth
 
-def graph_temperature(canvas, hours):
-    temperatures = [-4.5, -2., 0., 5., 2., 1., -0.3, 1.7, 1.9, 6.9, 7.8, 3.0]
-    temperatures_felt = [-5.3, -3.2, -1.0, 2.0, 1.0, -0.8, 1.3, -1.5, 0.1, 4.2, 5.0, 3.8]
 
-    _, temperatures_interp = interpolate_line(hours, temperatures, 3)
-    hours_extended, temperatures_felt_interp = interpolate_line(hours, temperatures_felt, 3)
+def graph_temperature(canvas, hours, temperatures, temperatures_feel):
 
-    canvas.plot(hours_extended, temperatures_interp, 'r', label="Temperatura rzeczywista", linewidth=4.0)
-    canvas.plot(hours_extended, temperatures_felt_interp, 'b', label="Temperatura odczuwalna", linewidth=4.0)
-    canvas.plot(np.array(hours).astype(float), temperatures, 'r.', markersize=15)
-    canvas.plot(np.array(hours).astype(float), temperatures_felt, 'b.', markersize=15)
+    canvas.plot(hours, temperatures, 'r', label="Temperatura rzeczywista", linewidth=4.0)
+    canvas.plot(hours, temperatures_feel, 'b', label="Temperatura odczuwalna", linewidth=4.0)
     canvas.legend(fontsize=14)
 
-    canvas.set_title('Poniedziałek, 24.06')
     canvas.set_ylabel('Temperatura [°C]')
 
-    canvas.set_xlim(float(hours[0]), float(hours[-1]))
+    canvas.margins(x=0.0)
 
     canvas.grid(True)
 
 
-def graph_rainfall(canvas, hours):
-
-    rainfall = [11, 12, 10, 15, 20, 10, 7, 0, 1, 3, 3, 6]
-    humidity = [0, 0, 0, 0, 15, 20, 32, 11, 8, 4, 0, 1]
-
-    hours_interp, humidity_interp = interpolate_line(np.array(hours).astype(float), np.array(humidity).astype(float), 3)
+def graph_rainfall(canvas, hours, rainfall, humidity):
 
     secondary_canvas = canvas.twinx()
-    secondary_canvas.plot(hours_interp, humidity_interp, color='#ff9914', linewidth=4.0)
-    canvas.bar(np.array(hours).astype(float), rainfall, width=0.7, color='#29bf12', edgecolor='#006400', linewidth=2.0)
+    secondary_canvas.plot(hours, humidity, color='#ff9914', linewidth=4.0)
+    canvas.bar(hours[:-1], rainfall[:-1], width=0.8, color='#29bf12', edgecolor='#006400', linewidth=4.0, align='edge')
+    canvas.set_ylim(bottom=0)
 
-    canvas.set_xlim(float(hours[0]), float(hours[-1]))
+    canvas.margins(x=0.0)
 
     canvas.set_ylabel('Opady [mm/h]')
     secondary_canvas.set_ylabel('Wilgotność [%]')
     canvas.grid(True)
 
 
-def graph_wind(canvas, hours):
+def graph_wind(canvas, hours, wind_speed, wind_gust):
 
-    wind_speed = [0, 5, 6, 18, 3, 20, 25, 41, 20, 11, 6, 2]
-    wind_gust = [5, 11, 21, 28, 27, 29, 45, 47, 26, 17, 10, 5]
+    canvas.plot(hours, wind_speed, linewidth=3.0, color='#4189e8')
+    canvas.hlines(wind_gust, xmin=[i - 0.2 for i, x in enumerate(hours)], xmax=[i + 0.2 for i, x in enumerate(hours)], colors='red', label='Podmuchy wiatru')
 
-    hours_interp, wind_interp = interpolate_line(hours, wind_speed, 3)
-
-    canvas.plot(hours_interp, wind_interp, linewidth=3.0, color='#4189e8')
-    canvas.plot(np.array(hours).astype(float), wind_speed, '.', markersize=15, color='#4189e8')
-    canvas.errorbar(np.array(hours).astype(float), wind_gust, xerr=0.45, fmt='none', color='#ed3a37')
-
-    canvas.set_xlim(float(hours[0]), float(hours[-1]))
+    canvas.margins(x=0.0)
 
     canvas.set_ylabel('Prędkość wiatru [km/h]')
 
     secondary_canvas = canvas.twinx()
-    secondary_canvas.plot(hours_interp, [x * 1000 / 3600 for x in wind_interp], linewidth=3.0, color='#4189e8')
+    secondary_canvas.plot(hours, [x * 1000 / 3600 for x in wind_speed], linewidth=3.0, color='#4189e8')
     secondary_canvas.set_ylim(canvas.get_ylim()[0] * 1000 / 3600, canvas.get_ylim()[1] * 1000 / 3600)
     secondary_canvas.set_ylabel('Prędkość wiatru [m/s]')
 
     canvas.grid(True)
 
 
 def generate_graphs_image():
-    figure, axes = plt.subplots(3, 1, figsize=(10, 9))
+    forecast = get_advanced_hourly_forecast()
 
-    hours = ['10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21']
+    figure, axes = plt.subplots(3, 1, figsize=(10, 9))
 
-    graph_temperature(axes[0], hours)
-    graph_rainfall(axes[1], hours)
-    graph_wind(axes[2], hours)
+    hours = forecast['hours']
+
+    axes[0].set_title(f'{get_day_of_week_capitalized()}, {get_today()}')
+
+    graph_temperature(
+        axes[0], hours,
+        temperatures=forecast['temperature'],
+        temperatures_feel=forecast['temperature_feel'])
+    graph_rainfall(
+        axes[1], hours,
+        rainfall=forecast['rainfall'],
+        humidity=forecast['humidity'])
+    graph_wind(
+        axes[2], hours,
+        wind_speed=forecast['wind_speed'],
+        wind_gust=forecast['wind_gust']
+    )
 
     plt.tight_layout()
     plt.savefig('./assets/generated_images/graphs.png', pil_kwargs={'compress_level': 7})

logic/logic.py

@@ -74,6 +74,16 @@ def get_channels():
     6: "niedziela"
 }
 
+days_of_week_capitalized = {
+    0: "Poniedziałek",
+    1: "Wtorek",
+    2: "Środa",
+    3: "Czwartek",
+    4: "Piątek",
+    5: "Sobota",
+    6: "Niedziela"
+}
+
 moon_phases = {
     "New": ":new_moon:",
     "WaningCrescent": ":waning_crescent_moon:",
@@ -135,6 +145,11 @@ def get_day_of_week():
     return days_of_week[day]
 
 
+def get_day_of_week_capitalized():
+    day = datetime.today().weekday()
+    return days_of_week_capitalized[day]
+
+
 def get_sunset_sunrise():
     data = download_forecast(type="daily")
     sunrise = data["DailyForecasts"][0]["Sun"]["Rise"][11:16]
@@ -163,7 +178,7 @@ async def get_birthday_message(our_server):
         for row in data:
             if row[0] == str(todays_date.day) and row[1] == str(todays_date.month):
                 i = 2
-                while row[i] != '':
+                while i < len(row) and row[i] != '':
                     ret += row[i] + ' - '
                     ret += f'*{row[i + 1]}* - '
                     ret += str(todays_date.year - int(row[i + 1])) + ' lat\n'
@@ -262,10 +277,28 @@ def get_hourly_forecast():
 def get_advanced_hourly_forecast():
     data = download_forecast(type='hourly')
 
-    result = {}
+    # hours, temperature, temperature_feel, rainfall, humidity, wind_speed, wind_gust
 
-    for one_hour in data:
-        pass
+    forecast = {
+        'hours': [],
+        'temperature': [],
+        'temperature_feel': [],
+        'rainfall': [],
+        'humidity': [],
+        'wind_speed': [],
+        'wind_gust': [],
+    }
+
+    for sample in data:
+        forecast['hours'].append(sample['DateTime'][11:13])
+        forecast['temperature'].append(sample['Temperature']['Value'])
+        forecast['temperature_feel'].append(sample['RealFeelTemperature']['Value'])
+        forecast['rainfall'].append(sample['TotalLiquid']['Value'])
+        forecast['humidity'].append(sample['RelativeHumidity'])
+        forecast['wind_speed'].append(sample['Wind']['Speed']['Value'])
+        forecast['wind_gust'].append(sample['WindGust']['Speed']['Value'])
+
+    return forecast
 
 
 def get_current_weather():