[1]:

from idf_analysis.idf_class import IntensityDurationFrequencyAnalyse
from idf_analysis.definitions import *
import pandas as pd
from os import path
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('bmh')

Intensity Duration Frequency Analyse¶

Parameter¶

series_kind:

SERIES.PARTIAL = Partielle Serie (partial duration series, PDS) (peak over threshold, POT)

SERIES.ANNUAL = Jährliche Serie (annual maximum series, AMS)

worksheet:

METHOD.KOSTRA: - DWA-A 531 - KOSTRA - empfohlen - Stützstellen: 60 min und 12 h

METHOD.CONVECTIVE_ADVECTIVE: - DWA-A 531 - Unterscheidung in überwiegend konvektiv und advektiv verursachte Starkregen - Stützstellen: 3 h und 24 h

METHOD.ATV: - ATV-A 121 - Stützstellen: 3 h und 48 h

extended_durations = Inkludiert die Dauerstufen [0.75d, 1d, 2d, 3d, 4d, 5d, 6d] in der Analyse (in d=Tage)

Standardmäßig berechnete Dauerstufen [5m, 10m, 15m, 20m, 30m, 45m, 60m, 1.5h, 3h, 4.5h, 6h, 7.5h, 10h, 12h]

[2]:

idf = IntensityDurationFrequencyAnalyse(series_kind=SERIES.PARTIAL, worksheet=METHOD.KOSTRA, extended_durations=True)

I used the rain-time-series from ehyd.gv.at with the ID 112086 (Graz-Andritz) created with the ehyd-tools package.

[3]:

data = pd.read_parquet('ehyd_112086.parquet')

[4]:

output_directory = 'ehyd_112086_idf_data'

[5]:

data.head()

[5]:

	precipitation
datetime
2007-09-18 11:09:00	0.1
2007-09-18 11:12:00	0.1
2007-09-18 11:13:00	0.1
2007-09-18 11:14:00	0.1
2007-09-18 11:20:00	0.1

[6]:

data.tail()

[6]:

	precipitation
datetime
2016-12-28 22:24:00	0.006
2016-12-28 22:25:00	0.092
2016-12-28 22:26:00	0.006
2016-12-28 22:45:00	0.090
2016-12-28 22:46:00	0.006

[7]:

idf.set_series(data['precipitation'])

Bei jeder neuen Berechnung werden Zwischenergebnisse erstellt, welche nur abhängig von der gewählten Serie series_kind und der angegebenen/benötigten Dauerstufen sind. Dieser Vorgang dauert einige Sekunden. Auserdem enthalten diese Zwischenergebnisse die Parameter, die zur Berechnung der Regenhöhe und Regenspende benötigt werden. Hier sind bereist die Berechnungsverfahren und Stückpunkte laut dem gewählten worksheet berücksichtigt.

Um Zeit zu sparen, gibt es die Möglichkeit, die Parameter zwischenzuspeichern und bei erneutem Aufrufen des Skripts werden diese Parameter nicht mehr berechnet, sondern aus der Datei gelesen.

[8]:

idf.auto_save_parameters(path.join(output_directory, 'idf_parameters.yaml'))

Abgerufen können diese Zwischenergebnisse mit:

[9]:

idf.parameters

[9]:

<idf_analysis.idf_parameters.IdfParameters at 0x1a767557310>

Berechnungen¶

[10]:

idf.depth_of_rainfall(duration=15, return_period=1)

[10]:

19.031596336052708

[11]:

print('Resultierende Regenhöhe h_N(T_n={t:0.1f}a, D={d:0.1f}min) = {h:0.2f} mm'
      ''.format(t=1, d=15, h=idf.depth_of_rainfall(15, 1)))

Resultierende Regenhöhe h_N(T_n=1.0a, D=15.0min) = 19.03 mm

[12]:

idf.rain_flow_rate(duration=15, return_period=1)

[12]:

211.46218151169674

[13]:

print('Resultierende Regenspende r_N(T_n={t:0.1f}a, D={d:0.1f}min) = {r:0.2f} L/(s*ha)'
      ''.format(t=1, d=15, r=idf.rain_flow_rate(15, 1)))

Resultierende Regenspende r_N(T_n=1.0a, D=15.0min) = 211.46 L/(s*ha)

[14]:

idf.r_720_1()

[14]:

11.410836729727

[15]:

idf.get_return_period(height_of_rainfall=10, duration=15)

[15]:

0.1430180144131331

[16]:

idf.get_duration(height_of_rainfall=10, return_period=1)

[16]:

5.433080747189968

[17]:

idf.result_table().round(2)

[17]:

	1	2	3	5	10	20	25	30	50	75	100
5	9.39	10.97	11.89	13.04	14.61	16.19	16.69	17.11	18.26	19.18	19.83
10	15.15	17.62	19.06	20.88	23.35	25.82	26.62	27.27	29.09	30.54	31.56
15	19.03	22.25	24.13	26.51	29.72	32.94	33.98	34.83	37.20	39.08	40.42
20	21.83	25.71	27.99	30.85	34.73	38.62	39.87	40.89	43.75	46.02	47.63
30	25.60	30.66	33.62	37.35	42.41	47.47	49.10	50.43	54.16	57.12	59.22
45	28.92	35.51	39.37	44.23	50.83	57.42	59.54	61.28	66.14	69.99	72.73
60	30.93	38.89	43.54	49.40	57.36	65.31	67.88	69.97	75.83	80.49	83.79
90	33.37	41.74	46.64	52.80	61.17	69.54	72.23	74.43	80.60	85.49	88.96
120	35.22	43.90	48.97	55.36	64.03	72.70	75.49	77.78	84.17	89.24	92.84
180	38.01	47.13	52.46	59.18	68.30	77.42	80.36	82.76	89.48	94.81	98.60
240	40.12	49.57	55.10	62.06	71.51	80.97	84.01	86.49	93.46	98.99	102.91
360	43.29	53.23	59.04	66.37	76.31	86.25	89.45	92.06	99.39	105.20	109.33
540	46.71	57.16	63.27	70.98	81.43	91.89	95.25	98.00	105.71	111.82	116.16
720	49.29	60.13	66.47	74.45	85.29	96.12	99.61	102.46	110.44	116.78	121.28
1080	54.41	64.97	71.15	78.94	89.50	100.06	103.46	106.24	114.02	120.20	124.58
1440	58.02	67.72	73.39	80.54	90.24	99.93	103.05	105.61	112.75	118.42	122.45
2880	66.70	77.41	83.68	91.57	102.29	113.00	116.45	119.26	127.16	133.42	137.87
4320	71.93	85.72	93.78	103.95	117.73	131.52	135.96	139.58	149.75	157.81	163.53
5760	78.95	95.65	105.42	117.72	134.43	151.13	156.50	160.89	173.20	182.97	189.90
7200	83.53	101.38	111.82	124.98	142.83	160.68	166.43	171.12	184.28	194.72	202.13
8640	85.38	104.95	116.40	130.82	150.38	169.95	176.25	181.40	195.82	207.27	215.39

[18]:

idf.result_table(add_names=True).round(2)

[18]:

return period (a)	1	2	3	5	10	20	25	30	50	75	100
frequency (1/a)	1.000	0.500	0.333	0.200	0.100	0.050	0.040	0.033	0.020	0.013	0.010
duration (min)
5	9.39	10.97	11.89	13.04	14.61	16.19	16.69	17.11	18.26	19.18	19.83
10	15.15	17.62	19.06	20.88	23.35	25.82	26.62	27.27	29.09	30.54	31.56
15	19.03	22.25	24.13	26.51	29.72	32.94	33.98	34.83	37.20	39.08	40.42
20	21.83	25.71	27.99	30.85	34.73	38.62	39.87	40.89	43.75	46.02	47.63
30	25.60	30.66	33.62	37.35	42.41	47.47	49.10	50.43	54.16	57.12	59.22
45	28.92	35.51	39.37	44.23	50.83	57.42	59.54	61.28	66.14	69.99	72.73
60	30.93	38.89	43.54	49.40	57.36	65.31	67.88	69.97	75.83	80.49	83.79
90	33.37	41.74	46.64	52.80	61.17	69.54	72.23	74.43	80.60	85.49	88.96
120	35.22	43.90	48.97	55.36	64.03	72.70	75.49	77.78	84.17	89.24	92.84
180	38.01	47.13	52.46	59.18	68.30	77.42	80.36	82.76	89.48	94.81	98.60
240	40.12	49.57	55.10	62.06	71.51	80.97	84.01	86.49	93.46	98.99	102.91
360	43.29	53.23	59.04	66.37	76.31	86.25	89.45	92.06	99.39	105.20	109.33
540	46.71	57.16	63.27	70.98	81.43	91.89	95.25	98.00	105.71	111.82	116.16
720	49.29	60.13	66.47	74.45	85.29	96.12	99.61	102.46	110.44	116.78	121.28
1080	54.41	64.97	71.15	78.94	89.50	100.06	103.46	106.24	114.02	120.20	124.58
1440	58.02	67.72	73.39	80.54	90.24	99.93	103.05	105.61	112.75	118.42	122.45
2880	66.70	77.41	83.68	91.57	102.29	113.00	116.45	119.26	127.16	133.42	137.87
4320	71.93	85.72	93.78	103.95	117.73	131.52	135.96	139.58	149.75	157.81	163.53
5760	78.95	95.65	105.42	117.72	134.43	151.13	156.50	160.89	173.20	182.97	189.90
7200	83.53	101.38	111.82	124.98	142.83	160.68	166.43	171.12	184.28	194.72	202.13
8640	85.38	104.95	116.40	130.82	150.38	169.95	176.25	181.40	195.82	207.27	215.39

To save the table as a csv:

[19]:

idf.result_table(add_names=True).round(2).to_csv(path.join(output_directory, 'idf_table_UNIX.csv'), sep=',', decimal='.', float_format='%0.2f')

[20]:

print(idf.result_table(add_names=True).round(2).to_string())

return period (a)    1       2       3       5       10      20      25      30      50      75      100
frequency (1/a)    1.000   0.500   0.333   0.200   0.100   0.050   0.040   0.033   0.020   0.013   0.010
duration (min)
5                   9.39   10.97   11.89   13.04   14.61   16.19   16.69   17.11   18.26   19.18   19.83
10                 15.15   17.62   19.06   20.88   23.35   25.82   26.62   27.27   29.09   30.54   31.56
15                 19.03   22.25   24.13   26.51   29.72   32.94   33.98   34.83   37.20   39.08   40.42
20                 21.83   25.71   27.99   30.85   34.73   38.62   39.87   40.89   43.75   46.02   47.63
30                 25.60   30.66   33.62   37.35   42.41   47.47   49.10   50.43   54.16   57.12   59.22
45                 28.92   35.51   39.37   44.23   50.83   57.42   59.54   61.28   66.14   69.99   72.73
60                 30.93   38.89   43.54   49.40   57.36   65.31   67.88   69.97   75.83   80.49   83.79
90                 33.37   41.74   46.64   52.80   61.17   69.54   72.23   74.43   80.60   85.49   88.96
120                35.22   43.90   48.97   55.36   64.03   72.70   75.49   77.78   84.17   89.24   92.84
180                38.01   47.13   52.46   59.18   68.30   77.42   80.36   82.76   89.48   94.81   98.60
240                40.12   49.57   55.10   62.06   71.51   80.97   84.01   86.49   93.46   98.99  102.91
360                43.29   53.23   59.04   66.37   76.31   86.25   89.45   92.06   99.39  105.20  109.33
540                46.71   57.16   63.27   70.98   81.43   91.89   95.25   98.00  105.71  111.82  116.16
720                49.29   60.13   66.47   74.45   85.29   96.12   99.61  102.46  110.44  116.78  121.28
1080               54.41   64.97   71.15   78.94   89.50  100.06  103.46  106.24  114.02  120.20  124.58
1440               58.02   67.72   73.39   80.54   90.24   99.93  103.05  105.61  112.75  118.42  122.45
2880               66.70   77.41   83.68   91.57  102.29  113.00  116.45  119.26  127.16  133.42  137.87
4320               71.93   85.72   93.78  103.95  117.73  131.52  135.96  139.58  149.75  157.81  163.53
5760               78.95   95.65  105.42  117.72  134.43  151.13  156.50  160.89  173.20  182.97  189.90
7200               83.53  101.38  111.82  124.98  142.83  160.68  166.43  171.12  184.28  194.72  202.13
8640               85.38  104.95  116.40  130.82  150.38  169.95  176.25  181.40  195.82  207.27  215.39

To create a color plot of the IDF curves:

[21]:

fig, ax = idf.result_figure(color=True, add_interim=False)

../_images/examples_example_python_api_29_0.png

To create a black/white plot of the IDF curves:

[22]:

fig, ax = idf.result_figure(color=False, add_interim=True)

../_images/examples_example_python_api_31_0.png

Intensity Duration Frequency Analyse¶

Parameter¶

Berechnungen¶

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