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create mode 100644 examples/example_heavy_rainfall_index.ipynb create mode 100644 examples/example_python_api.html create mode 100644 examples/example_python_api.ipynb create mode 100644 examples/example_python_api_extended.html create mode 100644 examples/example_python_api_extended.ipynb create mode 100644 genindex.html create mode 100644 index.html create mode 100644 objects.inv create mode 100644 py-modindex.html create mode 100644 search.html create mode 100644 searchindex.js diff --git a/.buildinfo b/.buildinfo new file mode 100644 index 0000000..4002c35 --- /dev/null +++ b/.buildinfo @@ -0,0 +1,4 @@ +# Sphinx build info version 1 +# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. +config: 2b19e505fcdb10a3dbc579c74e3d59d4 +tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/.nojekyll b/.nojekyll new file mode 100644 index 0000000..e69de29 diff --git a/README.html b/README.html new file mode 100644 index 0000000..3cd6271 --- /dev/null +++ b/README.html @@ -0,0 +1,319 @@ + + + + + + + + Intensity duration frequency analysis (based on KOSTRA) — Intensity Duration Frequency Analyse 0.2.3 documentation + + + + + + + + + + + + + +
+
+
+
+ +

© Institute of Urban Water Management and Landscape Water Engineering, Graz University of Technology and Markus Pichler

+
+

Intensity duration frequency analysis (based on KOSTRA)

+

license +PyPI

+

PyPI - Downloads +PyPI - Downloads +PyPI - Downloads

+

Heavy rain as a function of the duration and the return period acc. to DWA-A 531 (2012). +This program reads the measurement data of the rainfall +and calculates the distribution of the design rainfall as a function of the return period and the duration +for duration steps up to 12 hours (and more) and return period in a range of ‘0.5a ≤ T_n ≤ 100a’.

+

The guideline was used in the application KOSTRA-DWD.

+
+
+

Heavy rainfall data are among the most important planning parameters in water management and hydraulic engineering practice. In urban areas, for example, they are required as initial parameters for the design of rainwater drainage systems and in watercourses for the dimensioning of hydraulic structures. The accuracy of the target values of the corresponding calculation methods and models depends crucially on their accuracy. Their overestimation can lead to considerable additional costs in the structural implementation, their underestimation to an unacceptable, excessive residual risk of failure during the operation of water management and hydraulic engineering facilities. Despite the area-wide availability of heavy rainfall data through “Coordinated Heavy Rainfall Regionalisation Analyses” (KOSTRA), there is still a need for local station analyses, e.g. to evaluate the now extended data series, to evaluate recent developments or to classify local peculiarities in comparison to the KOSTRA data. However, this is only possible without restrictions if the methodological approach recommended in the worksheet is followed. In the DWA-A 531 worksheet, the main features of the ATVA 121 worksheet published in 1985 and of the identical DVWK-R 124 booklet of the DVWK Rules for Water Management “Heavy Rain Evaluation after Return Time and Duration” are retained. The aim of the revision is to take account of current developments without, however, calling into question the standardisation of the procedure for statistical heavy rain analyses which was intended at the time.

+
+

DWA-A 531 (2012) Translated with www.DeepL.com/Translator

+
+
+

An intensity-duration-frequency curve (IDF curve) is a mathematical function that relates the rainfall intensity with its duration and frequency of occurrence. These curves are commonly used in hydrology for flood forecasting and civil engineering for urban drainage design. However, the IDF curves are also analysed in hydrometeorology because of the interest in the time concentration or time-structure of the rainfall.

+
+

Wikipedia

+
+

This package was developed by Markus Pichler during his bachelor thesis and finalised it in the course of his employment at the Institute of Urban Water Management and Landscape Water Engineering.

+
+

Documentation

+

Read the docs here 📖.

+
+
+

Installation

+

This package is written in Python3. (use a version > 3.5)

+
pip install idf-analysis
+
+
+

Add the following tags to the command for special options:

+
    +

  • --user: To install the package only for the local user account (no admin rights needed)

    • +

  • --upgrade: To update the package

    • +
+
+

Windows

+

You have to install python first (i.e. the original python from the website).

+

To use the command-line-tool, it is advisable to add the path to your Python binary to the environment variables ^path1. +There is also an option during the installation to add python to the PATH automatically. ^path2

+
+
+

Linux/Unix

+

Python is pre-installed on most operating systems (as you probably knew).

+
+
+

Dependencies

+

Packages required for this program will be installed with pip during the installation process and can be seen +in the requirements.txt file.

+
+
+
+

Usage

+

The documentation of the python-API can be found here.

+
+
+

Commandline tool

+

The following commands show the usage for Linux/Unix systems. +To use these features on Windows you have to add python -m before each command.

+

To start the script use following commands in the terminal/Prompt

+

idf_analysis

+
+

idf_analysis -h

+
+
usage: __main__.py [-h] -i INPUT
+                   [-ws {ATV-A_121,DWA-A_531,DWA-A_531_advektiv}]
+                   [-kind {partial,annual}] [-t {>= 0.5 a and <= 100 a}]
+                   [-d {>= 5 min and <= 8640 min}] [-r {>= 0 L/s*ha}]
+                   [-h_N {>= 0 mm}] [--r_720_1] [--plot] [--export_table]
+
+heavy rain as a function of the duration and the return period acc. to DWA-A
+531 (2012) All files will be saved in the same directory of the input file but
+in a subfolder called like the inputfile + "_idf_data". Inside this folder a
+file called "idf_parameter.yaml"-file will be saved and contains interim-
+calculation-results and will be automatically reloaded on the next call.
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -i INPUT, --input INPUT
+                        input file with the rain time-series (csv or parquet)
+  -ws {ATV-A_121,DWA-A_531,DWA-A_531_advektiv}, --worksheet {ATV-A_121,DWA-A_531,DWA-A_531_advektiv}
+                        From which worksheet the recommendations for
+                        calculating the parameters should be taken.
+  -kind {partial,annual}, --series_kind {partial,annual}
+                        The kind of series used for the calculation.
+                        (Calculation with partial series is more precise and
+                        recommended.)
+  -t {>= 0.5 a and <= 100 a}, --return_period {>= 0.5 a and <= 100 a}
+                        return period in years (If two of the three variables
+                        (rainfall (height or flow-rate), duration, return
+                        period) are given, the third variable is calculated.)
+  -d {>= 5 min and <= 8640 min}, --duration {>= 5 min and <= 8640 min}
+                        duration in minutes (If two of the three variables
+                        (rainfall (height or flow-rate), duration, return
+                        period) are given, the third variable is calculated.)
+  -r {>= 0 L/(s*ha)}, --flow_rate_of_rainfall {>= 0 L/(s*ha)}
+                        rainfall in Liter/(s * ha) (If two of the three
+                        variables (rainfall (height or flow-rate), duration,
+                        return period) are given, the third variable is
+                        calculated.)
+  -h_N {>= 0 mm}, --height_of_rainfall {>= 0 mm}
+                        rainfall in mm or Liter/m^2 (If two of the three
+                        variables (rainfall (height or flow-rate), duration,
+                        return period) are given, the third variable is
+                        calculated.)
+  --r_720_1             design rainfall with a duration of 720 minutes (=12 h)
+                        and a return period of 1 year
+  --plot                get a plot of the idf relationship
+  --export_table        get a table of the most frequent used values
+
+
+
+
+

Example

+

Example Jupyter notebook for the commandline

+

Example Jupyter notebook for the python api

+

Example python skript

+
+

Example Files

+

Interim Results of the idf analysis

+
+
+

Example Plot

+

IDF-Curves-Plot

+
+
+

Example IDF table

+

IDF-Table

+

| return period in a
duration in min | 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 | +| 180 | 38.01 | 47.13 | 52.46 | 59.18 | 68.30 | 77.42 | 80.36 | 82.76 | 89.48 | 94.81 | 98.60 | +| 270 | 41.01 | 50.60 | 56.21 | 63.28 | 72.87 | 82.46 | 85.55 | 88.07 | 95.14 | 100.75 | 104.73 | +| 360 | 43.29 | 53.23 | 59.04 | 66.37 | 76.31 | 86.25 | 89.45 | 92.06 | 99.39 | 105.20 | 109.33 | +| 450 | 45.14 | 55.36 | 61.33 | 68.87 | 79.08 | 89.30 | 92.59 | 95.28 | 102.81 | 108.79 | 113.03 | +| 600 | 47.64 | 58.23 | 64.43 | 72.23 | 82.82 | 93.41 | 96.82 | 99.61 | 107.42 | 113.61 | 118.01 | +| 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 |

+
+
+
+

Background

+

Pseudocode for the parameter calculation.

+
For every duration step
+    calculating event sums
+    
+    if using annual event series:  # only recommeded for measurements longer than 20 year
+        converting every max event sum per year to a series
+        calculating parameters u and w using the gumbel distribution
+        
+    elif using partial event series:
+        converting the n (approximatly 2.72 x measurement duration in years) biggest event sums to a series
+        calculating parameters u and w using the exponential distribution
+    
+Splitting IDF curve formulation in to several duration ranges
+For each duration range:
+    For each parameter (u and w):
+        balancing the parameter over all duation steps (in the range) using a given formulation (creating parameters a and b)
+        # one-folded-logaritmic | two-folded-logarithmic | hyperbolic
+
+u(D) = f(a_u, b_u, D)
+w(D) = f(a_w, b_w, D)
+
+h(D,Tn) = u(D) + w(D) * ln(Tn)
+
+
+
+
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..bf51521 --- /dev/null +++ b/README.md @@ -0,0 +1,3 @@ +#GitHub Pages + +Last update of sphinx html documentation from [a4876fb](https://github.com/MarkusPic/intensity_duration_frequency_analysis/tree/a4876fbb571a1c45495f1db8ed79ab70910329f3) diff --git a/_images/examples_example_heavy_rainfall_index_15_0.png b/_images/examples_example_heavy_rainfall_index_15_0.png new file mode 100644 index 0000000000000000000000000000000000000000..f89dc6d11eae44c35cf7dd7a1c24edfd1f197923 GIT binary patch literal 42553 zcmZU)WmFtp6D^FpyF0->xVr|z;BLX)-Q8V+1}C_?yK8WFg1ZiIC(rw>@5jBpRK?R~0t)rtJBD26lA10m_GT{b zMoy++-;7)wZ0ucZERD(BOr4x9?d>?2zcRBkl3BR8I5_jMu-N|pKVY_ZGG}23-qQqK z1l~bL%NYy|5%b>(4wjjX2L@JLA}b-P>XCh(8>FY#Lsxi z2n9FIY=_9$KFVxI8a-i*h=|CDA}YSI$szmYXNeF?+U53} zr$0Xy|2oIR(NS~#JKkut&6RqGL5JCsQFLc)Z0wX@4Abp@U$HfhuCJ`DoR6$f*qfM` zG?4C}oWr|N(b3V>OUILVpL>KEf$canzMI%w;N>_@#^DVJ9bNM3OUKmJkTf)%R~@%i 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+
+
+
+ +

Source code for idf_analysis.event_series_analysis

+import warnings
+
+from math import floor, e
+
+import numpy as np
+import pandas as pd
+
+from scipy.stats import linregress
+
+from .definitions import PARAM, SERIES, COL
+from .sww_utils import year_delta, guess_freq, rain_events, agg_events
+
+
+

+[docs]
+def annual_series(rolling_sum_values, year_index):
+    """
+    Create an annual series of the maximum overlapping sum per year and calculate the "u" and "w" parameters.
+
+    acc. to DWA-A 531 chap. 5.1.5
+
+    Gumbel distribution | https://en.wikipedia.org/wiki/Gumbel_distribution
+
+    Args:
+        rolling_sum_values (numpy.ndarray): Array with maximum rolling sum per event per year.
+        year_index (numpy.ndarray): Array with year of the event.
+
+    Returns:
+        dict[str, float]: Parameter u and w from the annual series for a specific duration step as a tuple.
+    """
+    annually_series = pd.Series(rolling_sum_values).groupby(year_index).max().values
+    # annually_series = pd.Series(data=rolling_sum_values,
+    #                             index=events[COL.START].values).resample('AS').max().index
+    annually_series = np.sort(annually_series)[::-1]
+
+    sample_size = annually_series.size
+    index = np.arange(sample_size) + 1
+    x = -np.log(np.log((sample_size + 0.2) / (sample_size - index + 0.6)))
+
+    return _lin_regress(x, annually_series)

+
+
+
+def _plotting_formula(k, l, m):
+    """
+    plotting function acc. to DWA-A 531 chap. 5.1.3 for the partial series
+
+    Args:
+        k (float): running index
+        l (float): sample size
+        m (float): measurement period
+
+    Returns:
+        float: estimated empirical return period
+    """
+    return (l + 0.2) * m / ((k - 0.4) * l)
+
+
+

+[docs]
+def partial_series(rolling_sum_values, measurement_period):
+    """
+    Create a partial series of the largest overlapping sums and calculate the "u" and "w" parameters.
+
+    acc. to DWA-A 531 chap. 5.1.4
+
+    Exponential distribution | https://en.wikipedia.org/wiki/Exponential_distribution
+
+    Args:
+        rolling_sum_values (numpy.ndarray): Array with maximum rolling sum per event.
+        measurement_period (float): Measurement period in years.
+
+    Returns:
+        dict[str, float]: parameter u and w from the partial series for a specific duration step as a tuple
+    """
+    partially_series = rolling_sum_values
+    partially_series = np.sort(partially_series)[::-1]
+
+    # Use only the (2-3 multiplied with the number of measuring years) of the biggest
+    # values in the database (-> acc. to ATV-A 121 chap. 4.3; DWA-A 531 chap. 4.4).
+    # As a requirement for the extreme value distribution.
+    threshold_sample_size = int(floor(measurement_period * e))
+
+    if partially_series.size < threshold_sample_size:
+        warnings.warn('Fewer events in series than recommended for extreme value analysis. Use the results with mindfulness.')
+        threshold_sample_size = partially_series.size
+
+    partially_series = partially_series[:threshold_sample_size]
+
+    sample_size = threshold_sample_size
+    index = np.arange(sample_size) + 1
+    log_return_periods = np.log(_plotting_formula(index, sample_size, measurement_period))
+
+    return _lin_regress(log_return_periods, partially_series)

+
+
+
+def _lin_regress2(x, y):
+    # based on math - custom function
+    sample_size = len(x)
+    x_mean = np.mean(x)
+    y_mean = np.mean(y)
+
+    slope = ((x * y).sum() - sample_size * y_mean * x_mean) / \
+        ((x ** 2).sum() - sample_size * x_mean ** 2)
+
+    intercept = y_mean - slope * x_mean
+    return {PARAM.U: intercept, PARAM.W: slope}
+
+
+def _lin_regress(x, y):
+    # using scipy
+    res = linregress(x, y)
+    return {PARAM.U: res.intercept, PARAM.W: res.slope}
+
+
+def _improve_factor(interval):
+    """
+    correction factor acc. to DWA-A 531 chap. 4.3
+
+    Args:
+        interval (float): length of the interval: number of observations per duration
+
+    Returns:
+        float: correction factor
+    """
+    improve_factor = {1: 1.14,
+                      2: 1.07,
+                      3: 1.04,
+                      4: 1.03,
+                      5: 1.00,
+                      6: 1.00}
+
+    return np.interp(interval,
+                     list(improve_factor.keys()),
+                     list(improve_factor.values()))
+
+
+

+[docs]
+def iter_event_series(file_input, duration_steps):
+    """
+    Statistical analysis for each duration step.
+
+    acc. to DWA-A 531 chap. 5.1
+
+    Save the parameters of the distribution function as interim results.
+
+    Args:
+        file_input (pandas.Series): precipitation data
+        duration_steps (list[int] | numpy.ndarray): in minutes
+
+    Yields:
+        series: max rainfall intensity per event for each duration step
+    """
+    ts = file_input.copy()
+
+    # -------------------------------
+    base_frequency = guess_freq(ts.index)  # DateOffset/Timedelta
+
+    # ------------------------------------------------------------------------------------------------------------------
+    # acc. to DWA-A 531 chap. 4.2:
+    # The values must be independent of each other for the statistical evaluations.
+    # estimated four hours acc. (Schilling, 1984)
+    # for larger durations - use the duration as minimal gap
+    min_gap_schilling = pd.Timedelta(hours=4)
+
+    # --------------
+    # if
+    # use only duration for splitting events
+    # may increase design-rain-height of smaller durations
+
+    # -------------------------------
+    try:
+        from tqdm.auto import tqdm
+        pbar = tqdm(duration_steps, desc='Calculating Parameters u and w')
+    except ModuleNotFoundError:
+        pbar = duration_steps
+
+    for duration_integer in pbar:
+        pbar.set_description('Calculating Parameters u and w for duration {:0.0f}'.format(duration_integer))
+
+        duration = pd.Timedelta(minutes=duration_integer)
+
+        if duration < pd.Timedelta(base_frequency):
+            continue
+
+        if duration < min_gap_schilling:
+            min_gap = min_gap_schilling
+        else:
+            min_gap = duration
+
+        events = rain_events(ts, min_gap=min_gap)
+
+        # Correction factor acc. to DWA-A 531 chap. 4.3
+        improve = _improve_factor(duration / base_frequency)
+
+        roll_sum = ts.rolling(duration).sum()
+
+        year_index = events[COL.START].dt.year.values
+
+        # events[COL.rolling_sum_valuesAX_OVERLAPPING_SUM] = agg_events(events, roll_sum, 'max') * improve
+        yield agg_events(events, roll_sum, 'max') * improve, duration_integer, year_index

+
+
+
+

+[docs]
+def calculate_u_w(file_input, duration_steps, series_kind):
+    """
+    Statistical analysis for each duration step.
+
+    acc. to DWA-A 531 chap. 5.1
+
+    Save the parameters of the distribution function as interim results.
+
+    acc. to DWA-A 531 chap. 4.4: use the annual series only for measurement periods over 20 years
+
+
+    Args:
+        file_input (pandas.Series): precipitation data
+        duration_steps (list[int] | numpy.ndarray): in minutes
+        series_kind (str): 'annual' or 'partial'
+
+    Returns:
+        dict[int, dict]: with key=durations and values=dict(u, w)
+    """
+    # -------------------------------
+    # measuring time in years
+    measurement_start, measurement_end = file_input.index[[0, -1]]
+    measurement_period = (measurement_end - measurement_start) / year_delta(years=1)
+    if round(measurement_period, 1) < 10:
+        warnings.warn("The measurement period is too short. The results may be inaccurate! "
+                      "It is recommended to use at least ten years. "
+                      "(-> Currently {}a used)".format(measurement_period))
+
+    # -------------------------------
+    interim_results = {}
+
+    for rolling_sum_values, duration_integer, year_index in iter_event_series(file_input, duration_steps):
+
+        if series_kind == SERIES.ANNUAL:
+            interim_results[duration_integer] = annual_series(rolling_sum_values, year_index)
+        elif series_kind == SERIES.PARTIAL:
+            interim_results[duration_integer] = partial_series(rolling_sum_values, measurement_period)
+        else:
+            raise NotImplementedError
+
+    return interim_results

+
+
+ +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/_modules/idf_analysis/idf_backend.html b/_modules/idf_analysis/idf_backend.html new file mode 100644 index 0000000..df447c0 --- /dev/null +++ b/_modules/idf_analysis/idf_backend.html @@ -0,0 +1,436 @@ + + + + + + + idf_analysis.idf_backend — Intensity Duration Frequency Analyse 0.2.3 documentation + + + + + + + + + + + +
+
+
+
+ +

Source code for idf_analysis.idf_backend

+__author__ = "Markus Pichler"
+__credits__ = ["Markus Pichler"]
+__maintainer__ = "Markus Pichler"
+__email__ = "markus.pichler@tugraz.at"
+__version__ = "0.1"
+__license__ = "MIT"
+
+from collections import OrderedDict
+
+import pandas as pd
+import numpy as np
+from pathlib import Path
+
+from .definitions import *
+from .event_series_analysis import calculate_u_w, iter_event_series
+from .in_out import write_yaml, read_yaml
+from .parameter_formulation_class import FORMULATION_REGISTER, _Formulation, register_formulations_to_yaml
+
+
+

+[docs]
+class IdfParameters:
+    def __init__(self, series_kind=SERIES.PARTIAL, worksheet=METHOD.KOSTRA, extended_durations=False):
+        self.series_kind = series_kind
+
+        self._durations = None
+        self._set_default_durations(extended_durations)
+
+        self.parameters_series = {}  # parameters u and w (distribution function) from the event series analysis
+        self.parameters_final = {}  # parameters of the distribution function after the regression
+        # {lower duration bound in minutes: {parameter u or w: {'function': function-name}}}
+
+        self.set_parameter_approaches_from_worksheet(worksheet)
+
+    def __bool__(self):
+        return bool(self.parameters_series)
+
+    def calc_from_series(self, series):
+        u_w = calculate_u_w(series, self.durations, self.series_kind)
+        for p in PARAM.U_AND_W:
+            self.parameters_series[p] = np.array([d[p] for d in u_w.values()])
+        self._calc_params()
+
+    # def get_event_max_series(self, series):  # TODO
+    #     interim_results = {}
+    #
+    #     for rolling_sum_values, duration_integer, _ in iter_event_series(series, self.durations):
+    #         interim_results[duration_integer] = rolling_sum_values
+    #     return interim_results
+
+    def reverse_engineering(self, idf_table, linear_interpolation=False):
+        durations = idf_table.index.values
+        u = idf_table[1].values
+        w_dat = idf_table.sub(u, axis=0)
+        # --
+        w = []
+        for dur in durations:
+            dat = w_dat.loc[dur]
+            log_tn_i = np.log(dat.index.values)
+            w.append(sum(dat.values * log_tn_i) / sum(log_tn_i ** 2))
+
+        # -------
+        # ax = dat.plot(logx=True)
+        # fig = ax.get_figure()
+        # fig.show()
+        # print(results.summary())
+
+        self.durations = durations
+        self.parameters_series[PARAM.U] = np.array(u)
+        self.parameters_series[PARAM.W] = np.array(w)
+
+        if linear_interpolation:
+            self.clear_parameter_approaches()
+            self.add_parameter_approach(0, 'linear', 'linear')
+            self._calc_params()
+        else:
+            self._calc_params()
+
+    @property
+    def durations(self):
+        return np.array(self._durations)
+
+    @durations.setter
+    def durations(self, durations):
+        self._durations = list(durations)
+
+    def _set_default_durations(self, extended_durations=False):
+        # Suggestion from ATV-A 121 (ATV-DVWK-Regelwerk 2/2001)
+
+        # sampling points of the duration steps in minutes
+        duration_steps = [5, 10, 15, 20, 30, 45, 60, 90]
+        # self._duration_steps += [i * 60 for i in [3, 4.5, 6, 7.5, 10, 12, 18]]  # duration steps in hours
+        duration_steps += [i * 60 for i in [2, 3, 4, 6, 9, 12, 18]]  # duration steps in hours
+        if extended_durations:
+            duration_steps += [i * 60 * 24 for i in [1, 2, 3, 4, 5, 6]]  # duration steps in days
+
+        self.durations = duration_steps
+
+    def filter_durations(self, freq_minutes):
+        self.limit_duration(lowest=freq_minutes)
+
+    def limit_duration(self, lowest=None, highest=None):
+        bool_array = self.durations == False
+        if lowest is not None:
+            bool_array |= self.durations >= lowest
+        if highest is not None:
+            bool_array |= self.durations <= highest
+
+        self.durations = self.durations[bool_array]
+        for param in PARAM.U_AND_W:
+            if param in self.parameters_series:
+                self.parameters_series[param] = self.parameters_series[param][bool_array]
+
+

+[docs]
+    def set_parameter_approaches_from_worksheet(self, worksheet):
+        """
+        Set approaches depending on the duration and the parameter.
+
+        Args:
+            worksheet (str): worksheet name for the analysis:
+                - 'DWA-A_531'
+                - 'ATV-A_121'
+                - 'DWA-A_531_advektiv' (yet not implemented)
+
+        Returns:
+            list[dict]: table of approaches depending on the duration and the parameter
+        """
+        self.parameters_final = read_yaml(Path(__file__).parent / 'approaches' / (worksheet + '.yaml'))

+
+
+    def add_parameter_approach(self, duration_bound, approach_u, approach_w):
+        self.parameters_final[duration_bound] = {
+            PARAM.U: {PARAM.FUNCTION: approach_u},
+            PARAM.W: {PARAM.FUNCTION: approach_w},
+        }
+
+    def clear_parameter_approaches(self):
+        self.parameters_final = {}
+
+    def _iter_params(self):
+        lower_bounds = list(self.parameters_final.keys())
+        return zip(lower_bounds, lower_bounds[1:] + [np.inf])
+
+    def _calc_params(self, params_mean=None, duration_mean=None):
+        """
+        Calculate parameters a_u, a_w, b_u and b_w and add it to the dict
+
+        Args:
+            params_mean (dict[float]):
+            duration_mean (float):
+
+        Returns:
+            list[dict]: parameters
+        """
+        for dur_lower_bound, dur_upper_bound in self._iter_params():
+            param_part = (self.durations >= dur_lower_bound) & (self.durations <= dur_upper_bound)
+
+            if param_part.sum() == 1:
+                del self.parameters_final[dur_lower_bound]
+                # Only one duration step in this duration range.
+                # Only one value available in the series for this regression.
+                continue
+
+            params_dur = self.parameters_final[dur_lower_bound]
+
+            dur = self.durations[param_part]
+
+            for p in PARAM.U_AND_W:  # u or w
+                values_series = self.parameters_series[p][param_part]
+
+                if params_mean:
+                    param_mean = params_mean[p]
+                else:
+                    param_mean = None
+
+                # ----------------------------
+                if not isinstance(params_dur[p], _Formulation):
+                    approach = params_dur[p][PARAM.FUNCTION]
+                    if approach not in FORMULATION_REGISTER:
+                        raise NotImplementedError(f'{approach=}')
+
+                    params_dur[p] = _Formulation.from_dict(params_dur[p])  # init object
+
+                if params_dur[p].a is None or params_dur[p].b is None or param_mean is not None:
+                    params_dur[p].fit(dur, values_series, param_mean=param_mean, duration_mean=duration_mean)
+
+        # ----------------------------
+        # balance_parameter_change
+        # TODO only between Not linear ranges
+        if params_mean is None and (len(self.parameters_final) > 1):
+            print('_balance_parameter_change')
+            # the balance between the different duration ranges acc. to DWA-A 531 chap. 5.2.4
+            duration_step = list(self.parameters_final.keys())[1]
+            durations = np.array([duration_step - 0.001, duration_step + 0.001])
+
+            # if the interim results end here
+
+            if any(durations < self.durations.min()) or \
+                    any(durations > self.durations.max()):
+                return
+
+            u, w = self.get_u_w(durations)
+            self._calc_params(params_mean={PARAM.U: np.mean(u), PARAM.W: np.mean(w)}, duration_mean=duration_step)
+
+

+[docs]
+    def measured_points(self, return_periods, max_duration=None):
+        """
+        get the calculation results of the rainfall with u and w without the estimation of the formulation
+
+        Args:
+            return_periods (float | np.array | list | pd.Series): return period in [a]
+            max_duration (float): max duration in [min]
+
+        Returns:
+            pd.Series: series with duration as index and the height of the rainfall as data
+        """
+        interim_results = pd.DataFrame(index=self.durations)
+        interim_results.index.name = PARAM.DUR
+        interim_results[PARAM.U] = self.parameters_series[PARAM.U]
+        interim_results[PARAM.W] = self.parameters_series[PARAM.W]
+
+        if max_duration is not None:
+            interim_results = interim_results.loc[:max_duration].copy()
+
+        return pd.Series(index=interim_results.index,
+                         data=interim_results[PARAM.U] + interim_results[PARAM.W] * np.log(return_periods))

+
+
+    def get_duration_section(self, duration, param):
+        for lower, upper in self._iter_params():
+            if lower <= duration <= upper:
+                return self.parameters_final[lower][param]
+
+

+[docs]
+    def get_scalar_param(self, p, duration):
+        """
+
+        Args:
+            p (str): name of the parameter 'u' or 'w'
+            duration (float | int): in minutes
+
+        Returns:
+            (float, float): u, w
+        """
+        param = self.get_duration_section(duration, p)  # type: _Formulation
+
+        if param is None:
+            return np.NaN
+
+        return param.get_param(duration)

+
+
+

+[docs]
+    def get_array_param(self, p, duration):
+        """
+
+        Args:
+            p (str): name of the parameter 'u' or 'w'
+            duration (numpy.ndarray): in minutes
+
+        Returns:
+            (numpy.ndarray, numpy.ndarray): u, w
+        """
+        return np.vectorize(lambda d: self.get_scalar_param(p, d))(duration)

+
+
+

+[docs]
+    def get_u_w(self, duration):
+        """
+        calculate the u and w parameters depending on the durations
+
+        Args:
+            duration (numpy.ndarray| list | float | int): in minutes
+
+        Returns:
+            (float, float): u and w
+        """
+        if isinstance(duration, (list, np.ndarray)):
+            func = self.get_array_param
+        else:
+            func = self.get_scalar_param
+
+        return (func(p, duration) for p in PARAM.U_AND_W)

+
+
+

+[docs]
+    @classmethod
+    def from_interim_results_file(cls, interim_results_fn, worksheet=METHOD.KOSTRA):
+        """DEPRECIATED: for compatibility reasons. To use the old file and convert it to the new parameters file"""
+        interim_results = pd.read_csv(interim_results_fn, index_col=0, header=0)
+        p = cls(worksheet, interim_results)
+        return p

+
+
+    def to_yaml(self, filename):
+        register_formulations_to_yaml()
+
+        def to_basic(a):
+            return list([round(float(i), 4) for i in a])
+        data = OrderedDict({
+            PARAM.SERIES: self.series_kind,
+            PARAM.DUR: to_basic(self.durations),
+            PARAM.PARAMS_SERIES: {p: to_basic(l) for p, l in self.parameters_series.items()},
+            PARAM.PARAMS_FINAL: self.parameters_final
+        })
+        write_yaml(data, filename)
+
+    @classmethod
+    def from_yaml(cls, filename):
+        data = read_yaml(filename)
+        if isinstance(data, list):
+            return cls.from_yaml_depreciated(filename)
+        p = cls(series_kind=data[PARAM.SERIES])
+        p.durations = data[PARAM.DUR]
+        # list to numpy.array
+        p.parameters_series = {p: np.array(l) for p, l in data[PARAM.PARAMS_SERIES].items()}
+        p.parameters_final = data[PARAM.PARAMS_FINAL]
+        p._calc_params()
+        return p
+
+    @classmethod
+    def from_yaml_depreciated(cls, filename, series_kind=SERIES.PARTIAL):
+        data = read_yaml(filename)
+        p = cls(series_kind=series_kind)
+        p.durations = []
+        p.parameters_series = {PARAM.U: [], PARAM.W: []}
+        p.parameters_final = {}
+        for part in data:
+            start_dur = float(part['von'])
+            start_idx = 0
+            if start_dur in part[COL.DUR]:
+                start_idx = 1
+            p._durations += part[COL.DUR][start_idx:]
+            part_params = {}
+
+            for u_or_w in PARAM.U_AND_W:
+                part_params[u_or_w] = {PARAM.FUNCTION: part[u_or_w]}
+                if part[u_or_w] != APPROACH.LIN:
+                    part_params[u_or_w][PARAM.A] = part[f'{PARAM.A}_{u_or_w}']
+                    part_params[u_or_w][PARAM.B] = part[f'{PARAM.B}_{u_or_w}']
+
+                p.parameters_series[u_or_w] += part[PARAM.VALUES(u_or_w)][start_idx:]
+
+            p.parameters_final[start_dur] = part_params
+
+        p.parameters_series = {param: np.array(l) for param, l in p.parameters_series.items()}
+        if '.yaml' in filename:
+            os.rename(filename, filename.replace('.yaml', '_OLD.yaml'))
+            p.to_yaml(filename)
+        return p

+
+
+ +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/_modules/idf_analysis/idf_class.html b/_modules/idf_analysis/idf_class.html new file mode 100644 index 0000000..6eb64f3 --- /dev/null +++ b/_modules/idf_analysis/idf_class.html @@ -0,0 +1,984 @@ + + + + + + + idf_analysis.idf_class — Intensity Duration Frequency Analyse 0.2.3 documentation + + + + + + + + + + + +
+
+
+
+ +

Source code for idf_analysis.idf_class

+__author__ = "Markus Pichler"
+__credits__ = ["Markus Pichler"]
+__maintainer__ = "Markus Pichler"
+__email__ = "markus.pichler@tugraz.at"
+__version__ = "0.1"
+__license__ = "MIT"
+
+import warnings
+from os import path, mkdir
+from webbrowser import open as show_file
+import matplotlib.pyplot as plt
+from scipy.optimize import newton
+
+import numpy as np
+import pandas as pd
+
+from .arg_parser import heavy_rain_parser
+from .idf_backend import IdfParameters
+from .little_helpers import minutes_readable, height2rate, delta2min, rate2height, frame_looper, event_caption, \
+    event_caption_ger
+from .definitions import *
+from .in_out import import_series
+from .sww_utils import guess_freq, rain_events, agg_events, event_duration, resample_rain_series, rain_bar_plot, IdfError
+from .plot_helpers import idf_bar_axes
+from .synthetic_rainseries import _BlockRain, _EulerRain
+
+
+########################################################################################################################
+

+[docs]
+class IntensityDurationFrequencyAnalyse:
+    """
+    heavy rain as a function of the duration and the return period acc. to DWA-A 531 (2012)
+
+    This program reads the measurement data of the rainfall
+    and calculates the distribution of the rainfall as a function of the return period and the duration
+    
+    for duration steps up to 12 hours (and more) and return period in a range of '0.5a <= T_n <= 100a'
+
+    Attributes:
+        _series (pandas.Series): rain time-series
+        _freq (pandas.DateOffset): frequency of the rain series
+        _return_periods_frame (pandas.DataFrame): with return periods of all given durations
+        _rain_events (pandas.DataFrame):
+        _rainfall_sum_frame (pandas.DataFrame): with rain sums of all given durations
+
+    """
+    def __init__(self, series_kind=SERIES.PARTIAL, worksheet=METHOD.KOSTRA, extended_durations=False):
+        """
+        heavy rain as a function of the duration and the return period acc. to DWA-A 531 (2012)
+
+        This program reads the measurement data of the rainfall
+        and calculates the distribution of the rainfall as a function of the return period and the duration
+
+        for duration steps up to 12 hours (and more) and return period in a range of '0.5a <= T_n <= 100a'
+
+        Args:
+            series_kind (str): ['partial', 'annual']
+            worksheet (str): ['DWA-A_531', 'ATV-A_121', 'DWA-A_531_advektiv']
+            extended_durations (bool): add [720, 1080, 1440, 2880, 4320, 5760, 7200, 8640] minutes to the calculation
+        """
+        self._series = None
+        self._freq = None
+
+        #  how to calculate the idf curves
+        # IdfParameters.__init__(self, series_kind=series_kind, worksheet=worksheet,
+        #                                 extended_durations=extended_durations)
+        self._parameter = IdfParameters(series_kind=series_kind, worksheet=worksheet,
+                                        extended_durations=extended_durations)
+        # self._parameter = None  # type: IdfParameters #  how to calculate the idf curves
+
+        self._return_periods_frame = None
+        self._rain_events = None
+        self._rainfall_sum_frame = None
+
+        self._duration_steps_for_output = None
+
+    # __________________________________________________________________________________________________________________
+    @property
+    def series(self) -> pd.Series:
+        if self._series is None:
+            raise IdfError('No Series defined for IDF-Analysis!')
+        return self._series
+
+    @series.setter
+    def series(self, series: pd.Series):
+        self._series = series
+
+

+[docs]
+    def set_series(self, series):
+        """
+        set the series for the analysis
+
+        Args:
+            series (pandas.Series): precipitation time-series
+        """
+        if not isinstance(series, pd.Series):
+            raise IdfError('The series has to be a pandas Series.')
+
+        if not isinstance(series.index, pd.DatetimeIndex):
+            raise IdfError('The series has to have a DatetimeIndex.')
+
+        # if series.index.tz is not None:
+        #     series = remove_timezone(series)
+
+        self._freq = guess_freq(series.index)
+        freq_minutes = delta2min(self._freq)
+        self._parameter.filter_durations(freq_minutes)
+        self.series = series.replace(0, np.NaN).dropna()
+        self._return_periods_frame = None
+        self._rain_events = None
+        self._rainfall_sum_frame = None

+
+
+    # __________________________________________________________________________________________________________________
+    @property
+    def duration_steps(self):
+        """
+        get duration steps (in minutes) for the parameter calculation and basic evaluations
+
+        Returns:
+            list | numpy.ndarray: duration steps in minutes
+        """
+        # if not self.parameters:
+        #     raise IdfError('No Series defined for IDF-Analysis!')
+        return self.parameters.durations
+
+    @duration_steps.setter
+    def duration_steps(self, durations):
+        """
+        set duration steps (in minutes) for the parameter calculation and basic evaluations
+        Args:
+            durations (list | numpy.ndarray): duration steps in minutes
+        """
+        if not isinstance(durations, (list, np.ndarray)):
+            raise IdfError('Duration steps have to be {} got "{}"'.format((list, np.ndarray), type(durations)))
+        self.parameters.durations = durations
+
+    @property
+    def duration_steps_for_output(self):
+        """
+        get duration steps (in minutes) for the parameter calculation and basic evaluations
+
+        Returns:
+            list | numpy.ndarray: duration steps in minutes
+        """
+        # if not self.parameters:
+        #     raise IdfError('No Series defined for IDF-Analysis!')
+
+        if self._duration_steps_for_output is None:
+            self._duration_steps_for_output = self.duration_steps.copy()
+
+        return self._duration_steps_for_output
+
+    @duration_steps_for_output.setter
+    def duration_steps_for_output(self, durations):
+        """
+        set duration steps (in minutes) for the parameter calculation and basic evaluations
+        Args:
+            durations (list | numpy.ndarray): duration steps in minutes
+        """
+        if not isinstance(durations, (list, np.ndarray)):
+            raise IdfError('Duration steps have to be {} got "{}"'.format((list, np.ndarray), type(durations)))
+        self._duration_steps_for_output = durations
+
+    # __________________________________________________________________________________________________________________
+    @property
+    def parameters(self):
+        """
+        get the calculation parameters
+
+        calculation method depending on the used worksheet and on the duration
+        also the parameters for each method
+
+        to save some time and save the parameters with
+        :func:`IntensityDurationFrequencyAnalyse.write_parameters`
+        and read them later with :func:`IntensityDurationFrequencyAnalyse.read_parameters`
+
+        Returns:
+            IdfParameters: calculation parameters
+        """
+        if not self._parameter:
+            self._parameter.calc_from_series(self.series)
+        return self._parameter
+
+

+[docs]
+    def write_parameters(self, filename):
+        """
+        save parameters as yaml-file to save computation time.
+
+        Args:
+            filename (str): filename for the parameters yaml-file
+        """
+        self.parameters.to_yaml(filename)

+
+
+

+[docs]
+    def read_parameters(self, filename):
+        """
+        Read parameters from a .yaml-file to save computation time.
+
+        Extract interim results from parameters.
+
+        Args:
+            filename (str, Path): filename of the parameters yaml-file
+        """
+        self._parameter = IdfParameters.from_yaml(filename)

+
+
+

+[docs]
+    def auto_save_parameters(self, filename):
+        """Auto-save the parameters as a yaml-file to save computation time."""
+        if path.isfile(filename):
+            self.read_parameters(filename)
+        else:
+            from os import makedirs
+            parent = path.dirname(filename)
+            if parent and not path.isdir(parent):
+                makedirs(parent)
+            self.write_parameters(filename)

+
+
+    # __________________________________________________________________________________________________________________
+

+[docs]
+    def depth_of_rainfall(self, duration, return_period):
+        """
+        calculate the height of the rainfall h in L/m² = mm
+
+        Args:
+            duration (int | float | list | numpy.ndarray | pandas.Series): duration: in minutes
+            return_period (float): in years
+
+        Returns:
+            int | float | list | numpy.ndarray | pandas.Series: height of the rainfall h in L/m² = mm
+        """
+        if self.parameters.series_kind == SERIES.ANNUAL:
+            if return_period < 5:
+                print('WARNING: Using an annual series and a return period < 5 a will result in faulty values!')
+
+            if return_period <= 10:
+                return_period = np.exp(1.0 / return_period) / (np.exp(1.0 / return_period) - 1.0)
+
+            log_tn = -np.log(np.log(return_period / (return_period - 1.0)))
+
+        else:
+            log_tn = np.log(return_period)
+
+        u, w = self.parameters.get_u_w(duration)
+        return u + w * log_tn

+
+
+    # __________________________________________________________________________________________________________________
+

+[docs]
+    def rain_flow_rate(self, duration, return_period):
+        """
+        convert the height of rainfall to the specific rain flow rate in [l/(s*ha)]
+        if 2 array-like parameters are give, a element-wise calculation will be made.
+        So the length of the array must be the same.
+
+        Args:
+            duration (int | float | list | numpy.ndarray | pandas.Series): in minutes
+            return_period (float): in years
+
+        Returns:
+                int | float | list | numpy.ndarray | pandas.Series: specific rain flow rate in [l/(s*ha)]
+        """
+        height_of_rainfall = self.depth_of_rainfall(duration=duration, return_period=return_period)
+        return height2rate(height_of_rainfall=height_of_rainfall, duration=duration)

+
+
+    # __________________________________________________________________________________________________________________
+

+[docs]
+    def r_720_1(self):
+        """
+        rain flow rate in [l/(s*ha)] for a duration of 12h and a return period of 1 year
+
+        Returns:
+            float: rain flow rate in [l/(s*ha)]
+        """
+        return self.rain_flow_rate(duration=720, return_period=1)

+
+
+    # __________________________________________________________________________________________________________________
+

+[docs]
+    def get_return_period(self, height_of_rainfall, duration):
+        """
+        calculate the return period, when the height of rainfall and the duration are given
+
+        Args:
+            height_of_rainfall (float): in [mm]
+            duration (int | float | list | numpy.ndarray | pandas.Series): in minutes
+
+        Returns:
+            int | float | list | numpy.ndarray | pandas.Series: return period in years
+        """
+        u, w = self.parameters.get_u_w(duration)
+        return np.exp((height_of_rainfall - u) / w)

+
+
+    # __________________________________________________________________________________________________________________
+

+[docs]
+    def get_duration(self, height_of_rainfall, return_period):
+        """
+        calculate the duration, when the height of rainfall and the return period are given
+
+        Args:
+            height_of_rainfall (float): in [mm]
+            return_period (float): in years
+
+        Returns:
+            float: duration in minutes
+        """
+        return newton(lambda d: self.depth_of_rainfall(d, return_period) - height_of_rainfall, x0=1)

+
+
+    # __________________________________________________________________________________________________________________
+

+[docs]
+    def result_table(self, durations=None, return_periods=None, add_names=False):
+        """
+        Get an idf-table of rainfall depth with return periods as columns and durations as rows.
+
+        Args:
+            durations (list | numpy.ndarray): list of durations in minutes for the table
+            return_periods (list): list of return periods in years for the table
+            add_names (bool): weather to use expressive names as index-&column-label
+
+        Returns:
+            pandas.DataFrame: idf table
+        """
+        if durations is None:
+            durations = self.duration_steps_for_output
+
+        if return_periods is None:
+            return_periods = [1, 2, 3, 5, 10, 20, 25, 30, 50, 75, 100]
+
+        result_table = {}
+        for t in return_periods:
+            result_table[t] = self.depth_of_rainfall(durations, t)
+
+        result_table = pd.DataFrame(result_table, index=durations)
+
+        if add_names:
+            result_table.index.name = 'duration (min)'
+            result_table.columns = pd.MultiIndex.from_tuples([(rp, round(1 / rp, 3)) for rp in result_table.columns])
+            result_table.columns.names = ['return period (a)', 'frequency (1/a)']
+        return result_table

+
+
+    ####################################################################################################################
+

+[docs]
+    def result_figure(self, min_duration=5.0, max_duration=720.0, logx=False, return_periods=None, color=True, ax=None,
+                      linestyle=None, add_interim=False, alpha=1):
+        """
+        Create a plot with the idf-curves with duration on the x-axis and rainfall depth on the y-axis.
+
+        Args:
+            min_duration (float): Shortest duration on the plot.
+            max_duration (float): Longest duration on the plot.
+            logx (bool): Use a logarithmic scale on the x-axis.
+            return_periods (list[int] | Optional): List of return periods to plot. Default = [1, 2, 5, 10, 50, 100]
+            color (bool): Use color and a legend to differentiate between the return periods (default).
+                Otherwise, annotation text and black lines.
+            ax (plt.Axes): Axes to plot on. Default = create new one.
+            linestyle (str): Line-style for the plotted lines.
+            add_interim (bool): Add interim results from the series analysis as scatter points.
+            alpha (float): Alpha of the idf-lines in the plot.
+
+        Returns:
+            (plt.Figure, plt.Axes): figure and axes of the plot.
+        """
+        duration_steps = np.arange(min_duration, max_duration + 1, 1)
+
+        if return_periods is None:
+            return_periods = [1, 2, 5, 10, 50, 100]
+
+        table = self.result_table(durations=duration_steps, return_periods=return_periods)
+        if color:
+            table.columns.name = 'T$\\mathsf{_N}$ in a'
+        ax = table.plot(color=(None if color else 'black'), logx=logx, legend=color, ax=ax, ls=linestyle, alpha=alpha)
+
+        for _, return_time in enumerate(return_periods):
+            if add_interim:
+                p = self.parameters.measured_points(return_time, max_duration=max_duration)
+                # p = measured_points(self, return_time, max_duration=max_duration)
+                ax.plot(p, 'k' + 'x')
+
+            if not color:
+                x, y = list(table[return_time].tail(1).items())[0]
+                ax.text(x + 10, y, '{} a'.format(return_time), verticalalignment='center', horizontalalignment='left',
+                        # bbox=dict(facecolor='white', alpha=1.0, lw=1)
+                        )
+
+        ax.tick_params(axis='both', which='both', direction='out')
+        ax.set_xlabel('Duration D in min')
+        ax.set_ylabel('Rainfall h$\\mathsf{_N}$ in mm')
+        ax.set_title('IDF curves')
+
+        fig = ax.get_figure()  # type: plt.Figure
+
+        cm_to_inch = 2.54
+        fig.set_size_inches(h=21 / cm_to_inch, w=29.7 / cm_to_inch)  # (11.69, 8.27)
+        return fig, ax

+
+
+    ####################################################################################################################
+    @classmethod
+    def command_line_tool(cls):
+        user = heavy_rain_parser()
+
+        # --------------------------------------------------
+        # use the same directory as the input file and make as subdir with the name of the input_file + "_idf_data"
+        out = '{label}_idf_data'.format(label='.'.join(user.input.split('.')[:-1]))
+
+        if not path.isdir(out):
+            mkdir(out)
+            action = 'Creating'
+        else:
+            action = 'Using'
+
+        print('{} the subfolder "{}" for the interim- and final-results.'.format(action, out))
+
+        fn_pattern = path.join(out, 'idf_{}')
+
+        # --------------------------------------------------
+        idf = cls(series_kind=user.series_kind, worksheet=user.worksheet, extended_durations=True)
+
+        # --------------------------------------------------
+        parameters_fn = fn_pattern.format('parameters.yaml')
+
+        if path.isfile(parameters_fn):
+            print('Found existing interim-results in "{}" and using them for calculations.'.format(parameters_fn))
+        else:
+            print('Start reading the time-series {} for the analysis.'.format(user.input))
+            ts = import_series(user.input).replace(0, np.NaN).dropna()
+            # --------------------------------------------------
+            idf.set_series(ts)
+            print('Finished reading.')
+
+        # --------------------------------------------------
+        idf.auto_save_parameters(parameters_fn)
+
+        # --------------------------------------------------
+        h = user.height_of_rainfall
+        r = user.flow_rate_of_rainfall
+        d = user.duration
+        t = user.return_period
+
+        if r is not None:
+            if h is None and d is not None:
+                h = rate2height(rain_flow_rate=r, duration=d)
+
+            elif d is None and h is not None:
+                d = h / r * 1000 / 6
+
+        if user.r_720_1:
+            d = 720
+            t = 1
+
+        if any((h, d, t)):
+            if all((d, t)):
+                pass
+
+            elif all((d, h)):
+                t = idf.get_return_period(h, d)
+                print('The return period is {:0.1f} years.'.format(t))
+
+            elif all((h, t)):
+                d = idf.get_duration(h, t)
+                print('The duration is {:0.1f} minutes.'.format(d))
+
+            print('Resultierende Regenhöhe h_N(T_n={t:0.1f}a, D={d:0.1f}min) = {h:0.2f} mm'
+                  ''.format(t=t, d=d, h=idf.depth_of_rainfall(d, t)))
+            print('Resultierende Regenspende r_N(T_n={t:0.1f}a, D={d:0.1f}min) = {r:0.2f} L/(s*ha)'
+                  ''.format(t=t, d=d, r=idf.rain_flow_rate(d, t)))
+
+        # --------------------------------------------------
+        if user.plot:
+            import matplotlib.pyplot as plt
+            fig, ax = idf.result_figure()
+            plot_fn = fn_pattern.format('curves_plot.png')
+            fig.savefig(plot_fn, dpi=260)
+            plt.close(fig)
+            show_file(plot_fn)
+            print('Created the IDF-curves-plot and saved the file as "{}".'.format(plot_fn))
+
+        # --------------------------------------------------
+        if user.export_table:
+            table = idf.result_table(add_names=True)
+            print(table.round(2).to_string())
+            table_fn = fn_pattern.format('table.csv')
+            table.to_csv(table_fn, sep=';', decimal=',', float_format='%0.2f')
+            print('Created the IDF-curves-plot and saved the file as "{}".'.format(table_fn))
+
+    ####################################################################################################################
+

+[docs]
+    def get_rainfall_sum_frame(self, series=None, durations=None):
+        """
+        Get a rainfall sum frame for any series with the duration steps as columns.
+
+        Default: The time-series and the duration-steps of the analysis.
+
+        Args:
+            series (pandas.Series, Optional): rainfall time-series
+            durations (list, Optional): list of durations in minutes which are of interest (default: pre-defined durations)
+
+        Returns:
+            pandas.DataFrame: Rain sum depending on the duration per datetime-index.
+        """
+        if durations is None:
+            durations = self.duration_steps_for_output
+
+        if series is None:
+            if self._rainfall_sum_frame is not None:
+                return self._rainfall_sum_frame
+            ts = self.series.copy()
+            freq = self._freq
+        else:
+            freq = guess_freq(series.index)
+            ts = series.copy()
+            ts = ts.asfreq(freq).fillna(0)
+            # ts = series.replace(0, np.NaN).dropna()
+
+        df = pd.DataFrame(index=ts.index)
+        # df = {}
+
+        freq_num = delta2min(freq)
+
+        for d in frame_looper(ts.index.size, columns=durations, label='rainfall_sum'):
+            if d % freq_num != 0:
+                warnings.warn('Using durations (= {} minutes), '
+                              'which are not a multiple of the base frequency (= {} minutes) of the series, '
+                              'will lead to misinterpretations.'.format(d, freq_num))
+            df[d] = ts.rolling(pd.Timedelta(minutes=d)).sum()
+
+        # printable_names (bool): if durations should be as readable in dataframe, else in minutes
+        # df = df.rename(minutes_readable, axis=0)
+
+        return df#.round(2)

+
+
+    @property
+    def rainfall_sum_frame(self):
+        """
+        Get the rainfall sum over the whole time-series for the default duration steps.
+
+        Returns:
+            pandas.DataFrame: Rain sum depending on the duration per datetime-index.
+        """
+        if self._rainfall_sum_frame is None:
+            self._rainfall_sum_frame = self.get_rainfall_sum_frame()
+        return self._rainfall_sum_frame
+
+    ####################################################################################################################
+

+[docs]
+    def get_return_periods_frame(self, series=None, durations=None):
+        """
+        Get the return periods for any time-series with the duration steps as columns.
+
+        Default: The time-series and the duration-steps of the analysis.
+
+        Args:
+            series (pandas.Series, Optional): rainfall time-series
+            durations (list, Optional): Durations in minutes which are of interest (default: pre-defined durations)
+
+        Returns:
+            pandas.DataFrame: Return periods depending on the duration per datetime-index.
+        """
+        sums = self.get_rainfall_sum_frame(series=series, durations=durations)
+        df = pd.DataFrame(index=sums.index)
+        # df = {}
+        for d in frame_looper(sums.index.size, columns=sums.columns, label='return_periods'):
+            df[d] = self.get_return_period(height_of_rainfall=sums[d][sums[d] >= 0.1], duration=d)
+        return df#.fillna(0)#.round(2)

+
+
+    @property
+    def return_periods_frame(self):
+        """
+        Get the return periods over the whole time-series for the default duration steps.
+
+        Returns:
+            pandas.DataFrame: data-frame of return periods where the columns are the duration steps
+        """
+        if self._return_periods_frame is None:
+            self._return_periods_frame = self.get_return_periods_frame()
+        return self._return_periods_frame
+
+

+[docs]
+    def write_return_periods_frame(self, filename, **kwargs):
+        """save the return-periods dataframe as a parquet-file to save computation time."""
+        df = self.return_periods_frame.copy()
+        df.columns = df.columns.to_series().astype(str)
+        df.round(2).to_parquet(filename, **kwargs)

+
+
+

+[docs]
+    def read_return_periods_frame(self, filename, **kwargs):
+        """read the return-periods dataframe as a parquet-file to save computation time."""
+        df = pd.read_parquet(filename, **kwargs)
+        df.columns = df.columns.to_series().astype(int)
+        self._return_periods_frame = df

+
+
+

+[docs]
+    def auto_save_return_periods_frame(self, filename):
+        """auto-save the return-periods dataframe as a parquet-file to save computation time."""
+        if path.isfile(filename):
+            self.read_return_periods_frame(filename)
+        else:
+            self.write_return_periods_frame(filename)

+
+
+    ####################################################################################################################
+    @property
+    def rain_events(self):
+        """
+        get the all the rain events of the time-series
+
+        default minimal gap between events is 4 hours
+
+        Returns:
+            pandas.DataFrame: data-frame of events with start-, end-time and duration
+        """
+        if self._rain_events is None:
+            events = rain_events(self.series)
+            events[COL.DUR] = event_duration(events)
+            events[COL.LP] = agg_events(events, self.series, 'sum').round(1)
+            events[COL.LAST] = events[COL.START] - events[COL.END].shift()
+            # events = events.sort_values(by=COL.LP, ascending=False)
+            self._rain_events = events
+
+        return self._rain_events
+
+

+[docs]
+    def write_rain_events(self, filename, sep=';', decimal='.'):
+        """save the rain-events dataframe as a csv-file for external use or to save computation time."""
+        self.rain_events.to_csv(filename, index=False, sep=sep, decimal=decimal)

+
+
+

+[docs]
+    def read_rain_events(self, filename, sep=';', decimal='.'):
+        """read the rain-events dataframe as a csv-file to save computation time."""
+        events = pd.read_csv(filename, skipinitialspace=True, sep=sep, decimal=decimal)
+        events[COL.START] = pd.to_datetime(events[COL.START])
+        events[COL.END] = pd.to_datetime(events[COL.END])
+        events[COL.DUR] = pd.to_timedelta(events[COL.DUR])
+        events[COL.LAST] = pd.to_timedelta(events[COL.LAST])
+        self._rain_events = events

+
+
+

+[docs]
+    def auto_save_rain_events(self, filename, sep=';', decimal='.'):
+        """auto-save the rain-events dataframe as a csv-file to save computation time."""
+        if path.isfile(filename):
+            self.read_rain_events(filename, sep=sep, decimal=decimal)
+        else:
+            self.write_rain_events(filename, sep=sep, decimal=decimal)

+
+
+    def add_max_return_periods_to_events(self, events):
+        if COL.MAX_PERIOD not in events:
+            return_periods_frame = self.return_periods_frame
+
+            # maximum return period for every timestep
+            max_periods = return_periods_frame.max(axis=1).fillna(0)  # fill NaN -> weil < 0.1 gefiltert wurde
+
+            datetime_max = agg_events(events, max_periods, 'idxmax')
+            datetime_max = np.where(np.isnan(datetime_max), events[COL.START].values, datetime_max)
+            # alternative:
+            # [xv if c else yv for c, xv, yv in zip(np.isnan(datetime_max), events[COL.START].values, datetime_max)]
+            if return_periods_frame.index.tz is not None:
+                datetime_max = pd.DatetimeIndex(datetime_max).tz_localize('utc').tz_convert(return_periods_frame.index.tz)
+            events[COL.MAX_PERIOD] = max_periods[datetime_max].values
+
+            events[COL.MAX_PERIOD_DURATION] = return_periods_frame.loc[datetime_max].idxmax(axis=1, skipna=True).values
+
+

+[docs]
+    def add_max_intensities_to_events(self, events):
+        """
+        Add the maximum intensities for all duration steps to the events table.
+
+        Args:
+            events (pandas.DataFrame): events table
+
+        Returns:
+            pandas.DataFrame: events table including the columns with the maximum intensities
+        """
+        sum_frame = self.rainfall_sum_frame
+        for duration in self.duration_steps:
+            events[f'max_sum_{duration:0.0f}'] = agg_events(events, sum_frame[duration], 'max').round(2)
+        return events

+
+
+    ####################################################################################################################
+

+[docs]
+    def event_report(self, filename, min_event_rain_sum=25, min_return_period=0.5, durations=None):
+        """
+        create pdf file with the biggest rain events
+        for each event is represented by a plot of the rain series
+        and a IDF analysis where the return periods are calculated
+
+        Args:
+            filename (str): path (directory + filename) for the created pdf-report
+            min_event_rain_sum (float): only events with a bigger rain sum will be created
+            min_return_period (float): only events with a bigger return period will be analysed
+                                       (the plot will be created anyway)
+            durations (list[int]): analysed durations
+                        (default: [5, 10, 15, 20, 30, 45, 60, 90, 120, 180, 240, 360, 540, 720, 1080, 1440, 2880, 4320])
+        """
+        import matplotlib.pyplot as plt
+        from matplotlib.backends.backend_pdf import PdfPages
+        from tqdm.auto import tqdm
+
+        events = self.rain_events
+        self.add_max_return_periods_to_events(events)
+
+        main_events = events[(events[COL.LP] > min_event_rain_sum) & (events[COL.MAX_PERIOD] > min_return_period)].sort_values(by=COL.MAX_PERIOD, ascending=False).to_dict(
+            orient='index')
+
+        unit = 'mm'
+        column_name = 'Precipitation'
+
+        pdf = PdfPages(filename)
+
+        for _, event in tqdm(main_events.items()):
+            fig, caption = self.event_plot(event, min_return_period=min_return_period,
+                                           unit=unit, column_name=column_name)
+
+            # -------------------------------------
+            fig.get_axes()[0].set_title(caption + '\n\n\n')
+
+            # DIN A4
+            fig.set_size_inches(w=8.27, h=11.69)
+            # fig.tight_layout()
+            pdf.savefig(fig)
+            plt.close(fig)
+
+        pdf.close()

+
+
+    def event_plot(self, event, durations=None, unit='mm', column_name='Precipitation', min_return_period=1., german_caption=False, max_duration=None):
+        import matplotlib.pyplot as plt
+        if isinstance(event, pd.Series):
+            event = event.to_dict()
+
+        plot_range = slice(event[COL.START] - pd.Timedelta(self._freq), event[COL.END] + pd.Timedelta(self._freq))
+
+        return_periods_frame = self.return_periods_frame[plot_range]
+        if COL.MAX_PERIOD not in event:
+            event[COL.MAX_PERIOD] = return_periods_frame.max().max()
+            event[COL.MAX_PERIOD_DURATION] = return_periods_frame.max().idxmax()
+
+        sum_frame_event = self.rainfall_sum_frame[plot_range]
+
+        ts = self.series[plot_range].resample(self._freq).sum().fillna(0).copy()
+
+        # -------------------------------------
+        fig = plt.figure()
+
+        if event[COL.MAX_PERIOD] < min_return_period:
+            rain_ax = fig.add_subplot(111)
+
+        else:
+            if max_duration is None:
+                if durations is not None:
+                    max_dur = max(durations)
+                else:
+                    max_dur = max(self.duration_steps_for_output)
+            else:
+                max_dur = max_duration
+
+            return_periods_frame_extended = self.get_return_periods_frame(
+                self.series[event[COL.START] - pd.Timedelta(minutes=max_dur):
+                            event[COL.END] + pd.Timedelta(self._freq)].resample(self._freq).sum(),
+                durations=durations
+            ).round(1)
+
+            idf_bar_ax = fig.add_subplot(211)
+            idf_bar_ax = idf_bar_axes(idf_bar_ax, return_periods_frame_extended)
+            rain_ax = fig.add_subplot(212, sharex=idf_bar_ax)
+
+        # -------------------------------------
+        ts_sum, minutes = resample_rain_series(ts)
+        rain_ax = rain_bar_plot(ts_sum, rain_ax)
+        rain_ax.set_ylabel('{} in {}/{}min'.format(column_name, unit, minutes if minutes != 1 else ''))
+        rain_ax.set_xlim(ts.index[0], ts.index[-1])
+
+        return fig, (event_caption_ger(event) if german_caption else event_caption(event, unit))
+
+    ####################################################################################################################
+    def event_return_period_report(self, filename, min_return_period=1):
+        import matplotlib.pyplot as plt
+        from matplotlib.backends.backend_pdf import PdfPages
+        from tqdm.auto import tqdm
+
+        events = self.rain_events
+        self.add_max_return_periods_to_events(events)
+
+        main_events = events[events[COL.MAX_PERIOD] > min_return_period].sort_values(by=COL.MAX_PERIOD, ascending=False)
+
+        pdf = PdfPages(filename)
+
+        for _, event in tqdm(main_events.to_dict(orient='index').items()):
+            fig, ax = self.return_period_event_figure(event)
+
+            # -------------------------------------
+            # DIN A4
+            fig.set_size_inches(h=11.69 / 2, w=8.27)
+            # fig.tight_layout()
+            pdf.savefig(fig)
+            plt.close(fig)
+
+        pdf.close()
+
+    def return_period_event_figure(self, event):
+        import matplotlib.pyplot as plt
+        period_line = self.return_periods_frame[event[COL.START]:event[COL.END]].max()
+
+        # period_line[period_line < 0.75] = np.NaN
+        period_line = period_line.dropna()
+
+        ax = period_line.plot()  # type: plt.Axes
+
+        ax.set_title('rain event\n'
+                     'between {} and {}\n'
+                     'with a total sum of {:0.1f} mm\n'
+                     'and a duration of {}\n'
+                     'The maximum return period was {:0.2f}a\n'
+                     'at a duration of {}.'.format(event[COL.START].strftime('%Y-%m-%d %H:%M'),
+                                                   event[COL.END].strftime('%Y-%m-%d %H:%M'),
+                                                   event[COL.LP],
+                                                   event[COL.END] - event[COL.START],
+                                                   period_line.max(),
+                                                   period_line.idxmax()))
+        # ax.set_xscale('log')
+        # ax.set_yscale('log')
+        # print(ax.get_ylim())
+        # ax.set_ylim(0.01, 300)
+        # exit()
+        ax.set_xticks(period_line.index)
+        ax.set_xticklabels([minutes_readable(m) for m in period_line.index])
+        ax.set_xlabel('duration steps')
+        ax.set_ylabel('return period in years')
+        return ax.get_figure(), ax
+
+

+[docs]
+    @classmethod
+    def from_idf_table(cls, idf_table, worksheet=METHOD.KOSTRA):
+        """
+        Create an IDF-analysis-object based on an idf-tabel (i.e. from a given KOSTRA table)
+
+        Args:
+            idf_table (pandas.DataFrame): idf-table with index=Durations and columns=return Period and values=Rainheight
+            worksheet (str | optional): name of the worksheet to use.  default: 'KOSTRA'
+
+        Returns:
+            IntensityDurationFrequencyAnalyse: idf-object
+        """
+        idf = cls(worksheet=worksheet)
+        idf._parameter.reverse_engineering(idf_table)
+        return idf

+
+
+    @property
+    def model_rain_block(self):
+        """
+        Create a model block rain class.
+
+        Returns:
+            _BlockRain: Synthetic model block rain.
+        """
+        return _BlockRain(self)
+
+    @property
+    def model_rain_euler(self):
+        """
+        Create a model Euler rain class.
+
+        Returns:
+            _EulerRain: Synthetic model Euler rain.
+        """
+        return _EulerRain(self)

+
+
+ +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/_modules/idf_analysis/in_out.html b/_modules/idf_analysis/in_out.html new file mode 100644 index 0000000..a060376 --- /dev/null +++ b/_modules/idf_analysis/in_out.html @@ -0,0 +1,152 @@ + + + + + + + idf_analysis.in_out — Intensity Duration Frequency Analyse 0.2.3 documentation + + + + + + + + + + + +
+
+
+
+ +

Source code for idf_analysis.in_out

+__author__ = "Markus Pichler"
+__credits__ = ["Markus Pichler"]
+__maintainer__ = "Markus Pichler"
+__email__ = "markus.pichler@tugraz.at"
+__version__ = "0.1"
+__license__ = "MIT"
+
+import pandas as pd
+import yaml
+from collections import OrderedDict
+
+
+

+[docs]
+def import_series(filename, series_label='precipitation', index_label='datetime', csv_reader_args=None):
+    """
+
+    Args:
+        filename:
+        series_label:
+        index_label:
+        csv_reader_args: for example: sep="," or "." and decimal=";" or ","
+
+    Returns:
+        pandas.Series: precipitation series
+    """
+    if filename.endswith('csv'):
+        if csv_reader_args is None:
+            csv_reader_args = dict(sep=';', decimal=',')
+        try:
+            ts = pd.read_csv(filename, index_col=0, header=0, squeeze=True, **csv_reader_args)
+            ts.index = pd.to_datetime(ts.index)
+            ts.index.name = index_label
+            ts.name = series_label
+        except:
+            raise UserWarning('ERROR | '
+                              'Something is wrong with your csv format. The file should only include two columns. '
+                              'First column is the date and time index (prefered format is "YYYY-MM-DD HH:MM:SS") '
+                              'and second column the precipitation values in mm. '
+                              'As a separator use "{sep}" and as decimal sign use "{decimal}".'.format(**csv_reader_args))
+
+        return ts
+    elif filename.endswith('parquet'):
+        return pd.read_parquet(filename, columns=[series_label])[series_label].rename_axis(index_label, axis='index')
+    elif filename.endswith('pkl'):
+        return pd.read_pickle(filename).rename(series_label).rename_axis(index_label, axis='index')
+    else:
+        raise NotImplementedError('Sorry, but only csv, parquet and pickle files are implemented. '
+                                  'Maybe there will be more options soon.')

+
+
+# ------------------------------------------------------------------------------
+_mapping_tag = yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG
+
+
+def _dict_representer(dumper, data):
+    return dumper.represent_dict(data.items())
+
+
+def _dict_constructor(loader, node):
+    return OrderedDict(loader.construct_pairs(node))
+
+
+yaml.add_representer(OrderedDict, _dict_representer)
+yaml.add_constructor(_mapping_tag, _dict_constructor)
+
+
+def write_yaml(data, fn):
+    yaml.dump(data, open(fn, 'w'), default_flow_style=None, width=200)
+
+
+def read_yaml(filename):
+    return yaml.load(open(filename, 'r'), Loader=yaml.FullLoader)
+
+ +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/_modules/idf_analysis/little_helpers.html b/_modules/idf_analysis/little_helpers.html new file mode 100644 index 0000000..fedc5d8 --- /dev/null +++ b/_modules/idf_analysis/little_helpers.html @@ -0,0 +1,337 @@ + + + + + + + idf_analysis.little_helpers — Intensity Duration Frequency Analyse 0.2.3 documentation + + + + + + + + + + + +
+
+
+
+ +

Source code for idf_analysis.little_helpers

+__author__ = "Markus Pichler"
+__credits__ = ["Markus Pichler"]
+__maintainer__ = "Markus Pichler"
+__email__ = "markus.pichler@tugraz.at"
+__version__ = "0.1"
+__license__ = "MIT"
+
+import pandas as pd
+from pandas import Timedelta
+
+from .definitions import COL
+
+
+

+[docs]
+def delta2min(time_delta):
+    """
+    convert timedelta to float in minutes
+
+    Args:
+        time_delta (pandas.Timedelta, pandas.DateOffset):
+
+    Returns:
+        float: the timedelta in minutes
+    """
+    if isinstance(time_delta, pd.DateOffset):
+        time_delta = time_delta.delta
+    return int(time_delta.total_seconds() / 60)

+
+
+
+

+[docs]
+def minutes_readable(minutes):
+    """
+    convert the duration in minutes to a more readable form
+
+    Args:
+        minutes (float | int): duration in minutes
+
+    Returns:
+        str: duration as a string
+    """
+    if minutes <= 60:
+        return '{:0.0f} min'.format(minutes)
+    elif 60 < minutes < 60 * 24:
+        minutes /= 60
+        if minutes % 1:
+            fmt = '{:0.1f} h'
+        else:
+            fmt = '{:0.0f} h'
+        return fmt.format(minutes)
+    elif 60 * 24 <= minutes:
+        minutes /= 60 * 24
+        if minutes % 1:
+            fmt = '{:0.1f} d'
+        else:
+            fmt = '{:0.0f} d'
+        return fmt.format(minutes)
+    else:
+        return str(minutes)

+
+
+
+

+[docs]
+def duration_steps_readable(durations):
+    """
+    convert the durations to a more readable form
+
+    Args:
+        durations (list[int | float]): in minutes
+
+    Returns:
+        list[str]: of the readable duration list
+    """
+    return [minutes_readable(i) for i in durations]

+
+
+
+

+[docs]
+def height2rate(height_of_rainfall, duration):
+    """
+    calculate the specific rain flow rate in [l/(s*ha)]
+    if 2 array-like parameters are give, a element-wise calculation will be made.
+    So the length of the array must be the same.
+
+    Args:
+        height_of_rainfall (float | np.ndarray | pd.Series): height_of_rainfall: in [mm]
+        duration (float | np.ndarray | pd.Series): in minutes
+
+    Returns:
+        float | np.ndarray | pd.Series: specific rain flow rate in [l/(s*ha)]
+    """
+    return height_of_rainfall / duration * (1000 / 6)

+
+
+
+

+[docs]
+def rate2height(rain_flow_rate, duration):
+    """
+    convert the rain flow rate to the height of rainfall in [mm]
+    if 2 array-like parameters are give, a element-wise calculation will be made.
+    So the length of the array must be the same.
+
+    Args:
+        rain_flow_rate (float | np.ndarray | pd.Series): in [l/(s*ha)]
+        duration (float | np.ndarray | pd.Series): in minutes
+
+    Returns:
+        float | np.ndarray | pd.Series: height of rainfall in [mm]
+    """
+    return rain_flow_rate * duration / (1000 / 6)

+
+
+
+def frame_looper(size, columns, label='return periods'):
+    if size > 30000:  # if > 3 weeks, use a progressbar
+        try:
+            from tqdm.auto import tqdm
+            return tqdm(columns, desc=f'calculating {label} data-frame')
+        except ModuleNotFoundError:
+            return columns
+    else:
+        return columns
+
+
+def event_caption(event, unit='mm'):
+    caption = 'rain event\n'
+    if (COL.START in event) and (COL.END in event):
+        caption += f'between {event[COL.START]:%Y-%m-%d %H:%M} and '
+        if f'{event[COL.START]:%Y-%m-%d}' == f'{event[COL.END]:%Y-%m-%d}':
+            caption += f'{event[COL.END]:%H:%M}\n'
+        elif f'{event[COL.START]:%Y-%m-}' == f'{event[COL.END]:%Y-%m-}':
+            caption += f'{event[COL.END]:%d %H:%M}\n'
+        else:
+            caption += f'{event[COL.END]:%Y-%m-%d %H:%M}\n'
+
+    if COL.LP in event:
+        caption += f'with a total sum of {event[COL.LP]:0.1f} {unit}\n'
+
+    if COL.DUR in event:
+        caption += f' and a duration of {timedelta_readable(event[COL.DUR])}'
+
+    caption += '.\n'
+
+    if COL.MAX_PERIOD in event:
+        caption += f' The maximum return period was {return_period_formatter(event[COL.MAX_PERIOD])} a\n'
+
+        if COL.MAX_PERIOD_DURATION in event:
+            caption += f' at a duration of {minutes_readable(event[COL.MAX_PERIOD_DURATION])}.'
+
+    return caption
+
+
+def event_caption_ger(event, unit='mm'):
+    caption = 'Regenereignis\n'
+    if (COL.START in event) and (COL.END in event):
+        caption += f'zwischen {event[COL.START]:%Y-%m-%d %H:%M} und '
+        if f'{event[COL.START]:%Y-%m-%d}' == f'{event[COL.END]:%Y-%m-%d}':
+            caption += f'{event[COL.END]:%H:%M}\n'
+        elif f'{event[COL.START]:%Y-%m-}' == f'{event[COL.END]:%Y-%m-}':
+            caption += f'{event[COL.END]:%d %H:%M}\n'
+        else:
+            caption += f'{event[COL.END]:%Y-%m-%d %H:%M}\n'
+
+    if COL.LP in event:
+        caption += f'mit einer Regensumme von {event[COL.LP]:0.1f} {unit}\n'
+
+    if COL.DUR in event:
+        caption += f' und einer Dauer von {timedelta_readable(event[COL.DUR])}'
+
+    caption += '.\n'
+
+    if COL.MAX_PERIOD in event:
+        caption += f' Die maximale Wiederkehrperiode war {return_period_formatter(event[COL.MAX_PERIOD])} a\n'
+
+        if COL.MAX_PERIOD_DURATION in event:
+            caption += f' bei einer Dauerstufe von {minutes_readable(event[COL.MAX_PERIOD_DURATION])}.'
+
+    return caption
+
+
+def return_period_formatter(t):
+    if t < 1:
+        return '< 1'
+    elif t > 200:
+        return '$\\gg$ 100'
+    elif t > 100:
+        return '> 100'
+    else:
+        return f'{t:0.1f}'
+
+
+

+[docs]
+def timedelta_components_plus(td, min_freq='T'):
+    """Schaltjahre nicht miteinbezogen"""
+    l = []
+
+    # years, weeks
+    days_year = 365
+    days_week = 7
+
+    for component, value in td.round(min_freq).components._asdict().items():
+        if component == 'days':
+            years, value = value // days_year, value % days_year
+            l.append([int(years), 'years'])
+
+            value -= years // 4
+
+            weeks, value = value // days_week, value % days_week
+            l.append([int(weeks), 'weeks'])
+
+        l.append([value, component])
+    return l

+
+
+
+def timedelta_components_readable(l, short=False, sep=', '):
+    s = sep.join(
+        ['{}{}{}'.format(v, '' if short else ' ', c[0] if short else (c if v > 1 else c[:-1])) for v, c in l if v > 0])
+    if not short:
+        # replace last "," with "and"
+        s = ' and '.join(s.rsplit(sep, 1))
+    return s
+
+
+

+[docs]
+def timedelta_readable(td, min_freq='T', short=False, sep=', '):
+    """Schaltjahre nicht miteinbezogen"""
+    return timedelta_components_readable(timedelta_components_plus(td, min_freq), short=short, sep=sep)

+
+
+
+def timedelta_readable2(d1, d2, min_freq='T', short=False, sep=', '):
+    td = d2 - d1
+
+    years = None
+    if td > Timedelta(days=365):
+        d2_new = d2.replace(year=d1.year)
+
+        if d2_new < d1:
+            d2_new = d2_new.replace(year=d1.year + 1)
+
+        years = d2.year - d2_new.year
+
+        td = d2_new - d1
+
+    l = timedelta_components_plus(td, min_freq)
+
+    if years is not None:
+        l[0][0] = years
+
+    return timedelta_components_readable(l, short=short, sep=sep)
+
+ +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/_modules/idf_analysis/parameter_formulations.html b/_modules/idf_analysis/parameter_formulations.html new file mode 100644 index 0000000..6a75ad2 --- /dev/null +++ b/_modules/idf_analysis/parameter_formulations.html @@ -0,0 +1,205 @@ + + + + + + + idf_analysis.parameter_formulations — Intensity Duration Frequency Analyse 0.2.3 documentation + + + + + + + + + + + +
+
+
+
+ +

Source code for idf_analysis.parameter_formulations

+import numpy as np
+
+from .definitions import APPROACH
+
+
+

+[docs]
+def folded_log_formulation(duration, param, case, param_mean=None, duration_mean=None):
+    """
+
+    Args:
+        duration:
+        param:
+        case:
+        param_mean:
+        duration_mean:
+
+    Returns:
+
+    """
+    if param_mean and duration_mean:
+        mean_ln_duration = np.log(duration_mean)
+        mean_ln_param = np.log(param_mean)
+    else:
+        param_mean = param.mean()
+        mean_ln_param = np.log(param).mean()
+        mean_ln_duration = np.log(duration).mean()
+
+    divisor = ((np.log(duration) - mean_ln_duration) ** 2).sum()
+
+    if case == APPROACH.LOG2:
+        # for the twofold formulation
+        b = ((np.log(param) - mean_ln_param) * (np.log(duration) - mean_ln_duration)).sum() / divisor
+        a = mean_ln_param - b * mean_ln_duration
+
+    elif case == APPROACH.LOG1:
+        # for the onefold formulation
+        b = ((param - param_mean) * (np.log(duration) - mean_ln_duration)).sum() / divisor
+        a = param_mean - b * mean_ln_duration
+
+    else:
+        raise NotImplementedError
+
+    return float(a), float(b)

+
+
+
+

+[docs]
+def hyperbolic_formulation(duration: np.array, param: np.array, a_start=20.0, b_start=15.0, param_mean=None, duration_mean=None):
+    """
+    Computes the hyperbolic formulation using the given parameters.
+
+    Args:
+        duration (np.array): Array of durations.
+        param (np.array): Array of parameters.
+        a_start (float): Initial value for 'a' parameter.
+        b_start (float): Initial value for 'b' parameter.
+        param_mean (float, optional): Mean value of parameters. Defaults to None.
+        duration_mean (float, optional): Mean value of durations. Defaults to None.
+
+    Returns:
+        tuple[float, float]: Computed value of 'a' and 'b'.
+    """
+
+    def get_param(dur, par, a_, b_):
+        """
+        Computes 'a' and 'b' parameters based on given durations and parameters.
+
+        Args:
+            dur (np.array): Array of durations.
+            par (np.array): Array of parameters.
+            a_ (float): Current value of 'a' parameter.
+            b_ (float): Current value of 'b' parameter.
+
+        Returns:
+            tuple[float, float]: Computed value of 'a' and 'b'.
+        """
+        i = -a_ / (dur + b_)
+        if param_mean:
+            i_mean = - param_mean / duration_mean
+            param_mean_ = param_mean
+        else:
+            i_mean = i.mean()
+            param_mean_ = par.mean()
+
+        b_ = ((par - param_mean_) * (i - i_mean)).sum() / ((i - i_mean) ** 2).sum()
+        a_ = param_mean_ - b_ * i_mean
+        return a_, b_
+
+    # ------------------------------------------------------------------------------------------------------------------
+    iteration_steps = 0
+
+    a = a_start
+    b = b_start
+
+    conditions = True
+    while conditions:
+        conditions = False
+
+        a_s = a
+        b_s = b
+        a, b = get_param(duration, param, a, b)
+        conditions = (abs(a - a_s) > 0.005) or (abs(b - b_s) > 0.005) or conditions
+        a = (a + a_s) / 2
+        b = (b + b_s) / 2
+
+        iteration_steps += 1
+    return float(a), float(b)

+
+
+
+def hyperbolic_function(D, a, b):
+    return a * D / (D + b)
+
+
+def hyperbolic_formulation_chatgpt_opt(duration: np.array, param: np.array, a_start=20.0, b_start=15.0, param_mean=None, duration_mean=None):
+    # TODO: param_mean and duration_mean for parameter - balance
+    from scipy.optimize import curve_fit
+    # Perform the curve fitting
+    initial_guess = [a_start, b_start]  # initial guess for a and b
+    fit_params, _ = curve_fit(hyperbolic_function, duration, param, p0=initial_guess)
+
+    # Extract the optimized parameter values
+    a_fit, b_fit = fit_params
+    return float(a_fit), float(b_fit)
+
+ +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/_modules/idf_analysis/plot_helpers.html b/_modules/idf_analysis/plot_helpers.html new file mode 100644 index 0000000..1d506b1 --- /dev/null +++ b/_modules/idf_analysis/plot_helpers.html @@ -0,0 +1,190 @@ + + + + + + + idf_analysis.plot_helpers — Intensity Duration Frequency Analyse 0.2.3 documentation + + + + + + + + + + + +
+
+
+
+ +

Source code for idf_analysis.plot_helpers

+__author__ = "Markus Pichler"
+__credits__ = ["Markus Pichler"]
+__maintainer__ = "Markus Pichler"
+__email__ = "markus.pichler@tugraz.at"
+__version__ = "0.1"
+__license__ = "MIT"
+
+import pandas as pd
+
+from .definitions import COL
+from .little_helpers import duration_steps_readable
+from .sww_utils import guess_freq, rain_events, event_duration
+
+RETURN_PERIOD_COLORS = {
+    # 0.5: '#e0ffff',  # 'lightcyan',
+    1: '#00ffff',  # 'cyan',
+    2: '#add8e6',  # 'lightblue',
+    5: '#0000ff',  # 'blue',
+    10: '#ffff00',  # 'yellow',
+    20: '#ffa500',  # 'orange',
+    50: '#ff0000',  # 'red',
+    100: '#ff00ff',  # 'magenta',
+}
+
+
+

+[docs]
+def idf_bar_axes(ax, idf_table, return_period_colors=RETURN_PERIOD_COLORS):
+    """
+    create
+
+    Args:
+        ax (matplotlib.pyplot.Axes):
+        idf_table (pandas.DataFrame):
+        return_period_colors (dict): color of each return period {return period: color}
+
+    Returns:
+        matplotlib.pyplot.Axes:
+    """
+    return_periods = list(return_period_colors.keys())
+    color_return_period = list(return_period_colors.values())
+
+    # legend
+    from matplotlib.lines import Line2D
+    custom_lines = [Line2D([0], [0], color=c, lw=4) for c in color_return_period]
+    names = ['{}a'.format(t) for t in return_periods]
+    ax.legend(custom_lines, names, bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=len(color_return_period),
+              mode="expand", borderaxespad=0., title='return periods')
+
+    duration_size = len(idf_table.columns)
+    # labels for the y axis
+    durations_index = range(duration_size)
+    dh = 1
+    ax.set_yticks([i + dh/2 for i in durations_index], minor=True)
+    ax.set_yticks(list(durations_index), minor=False)
+
+    ax.set_yticklabels(duration_steps_readable(idf_table.columns), minor=True)
+    ax.set_yticklabels([''] * duration_size, minor=False)
+    ax.set_ylabel('duration of the design rainfall')
+
+    # for the relative start time
+    freq = guess_freq(idf_table.index)
+    start_period = idf_table.index[0].to_period(freq).ordinal
+
+    # idf_table.index = idf_table.index - idf_table.index[0]
+
+    min_duration = pd.Timedelta(minutes=1)
+
+    for hi, d in enumerate(idf_table.columns):
+        tn = idf_table[d]
+
+        for t in return_periods:
+            c = return_period_colors[t]
+            # not really a rain event, but the results are the same
+            # tab2 = rain_events(tn, ignore_rain_below=t, min_gap=pd.Timedelta(minutes=d))
+            tab = rain_events(tn, ignore_rain_below=t, min_gap=freq)
+            # if tab.size != tab2.size:
+            #     print()
+            if tab.empty:
+                continue
+
+            if 1:
+                durations = ((event_duration(tab) + freq) / min_duration).tolist()
+                rel_starts = ((tab[COL.START] - idf_table.index[0]) / min_duration + start_period).tolist()
+                bar_x = list(zip(rel_starts, durations))
+            else:
+                tab[COL.DUR] = event_duration(tab) / min_duration
+                bar_x = [(r[COL.START] / min_duration + start_period, r[COL.DUR]) for _, r in tab.iterrows()]
+
+            ax.broken_barh(bar_x, (hi, dh), facecolors=c)
+
+    ax.tick_params(axis='y', which='minor', length=0)
+    ax.grid(axis='y', which='major')
+
+    ax.set_ylim(0, duration_size)
+    ax.set_xticklabels([])
+    from matplotlib.ticker import NullFormatter
+    ax.xaxis.set_major_formatter(NullFormatter())
+
+    # ---
+    duration_steps = idf_table.columns.values
+    duration_steps_middle_to_long = duration_steps[duration_steps > 2*60]
+    if duration_steps_middle_to_long.size:
+        # (k)urzzeitige Summationen, d. h. der Dauerstufen von 5 Minuten bis 2 Stunden
+        ax.axhline(duration_steps.tolist().index(duration_steps_middle_to_long[0]), color='black')
+        duration_steps_long = duration_steps_middle_to_long[duration_steps_middle_to_long > 3*60*24]
+        if duration_steps_long.size:
+            # (m)ittelfristige Summationen, d. h. der Dauerstufen von 3 Stunden bis 3 Tagen.
+            ax.axhline(duration_steps.tolist().index(duration_steps_long[0]), color='black')
+    return ax

+
+
+ +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/_modules/idf_analysis/sww_utils.html b/_modules/idf_analysis/sww_utils.html new file mode 100644 index 0000000..2fbf445 --- /dev/null +++ b/_modules/idf_analysis/sww_utils.html @@ -0,0 +1,321 @@ + + + + + + + idf_analysis.sww_utils — Intensity Duration Frequency Analyse 0.2.3 documentation + + + + + + + + + + + +
+
+
+
+ +

Source code for idf_analysis.sww_utils

+__author__ = "David Camhy, Markus Pichler"
+__copyright__ = "Copyright 2018, University of Technology Graz"
+__credits__ = ["David Camhy", "Markus Pichler"]
+__license__ = "MIT"
+__version__ = "1.0.0"
+__maintainer__ = "David Camhy, Markus Pichler"
+
+import numpy as np
+import pandas as pd
+from pandas.tseries.frequencies import to_offset
+
+from .definitions import COL
+
+
+

+[docs]
+class IdfError(Exception):
+    """Some Error Within this Package"""

+
+
+
+########################################################################################################################
+

+[docs]
+def guess_freq(date_time_index, default=pd.Timedelta(minutes=1)):
+    """
+    guess the frequency by evaluating the most often frequency
+
+    Args:
+        date_time_index (pandas.DatetimeIndex): index of a time-series
+        default (pandas.Timedelta):
+
+    Returns:
+        pandas.DateOffset: frequency of the date-time-index
+    """
+    freq = date_time_index.freq
+    if pd.notnull(freq):
+        return to_offset(freq)
+
+    if not len(date_time_index) <= 3:
+        freq = pd.infer_freq(date_time_index)  # 'T'
+
+        if pd.notnull(freq):
+            return to_offset(freq)
+
+        delta_series = date_time_index.to_series().diff(periods=1).bfill()  # .fillna(method='backfill')
+        counts = delta_series.value_counts()
+        counts.drop(pd.Timedelta(minutes=0), errors='ignore')
+
+        if counts.empty:
+            delta = default
+        else:
+            delta = counts.index[0]
+            if delta == pd.Timedelta(minutes=0):
+                delta = default
+    else:
+        delta = default
+
+    return to_offset(delta)

+
+
+
+########################################################################################################################
+def year_delta(years):
+    return pd.Timedelta(days=365.25 * years)
+
+
+########################################################################################################################
+

+[docs]
+def rain_events(series, ignore_rain_below=0.01, min_gap=pd.Timedelta(hours=4)):
+    """
+    get rain events as a table with start and end times
+
+    Args:
+        series (pandas.Series): rain series
+        ignore_rain_below (float): where it is considered as rain
+        min_gap (pandas.Timedelta): 4 hours of no rain between events
+
+    Returns:
+        pandas.DataFrame: table of the rain events
+    """
+    # best OKOSTRA adjustment with 0.0
+    # by ignoring 0.1 mm the results are getting bigger
+
+    # remove values below a from the database
+    temp = series[series >= ignore_rain_below].index.to_series()
+
+    if temp.empty:
+        return pd.DataFrame()
+
+    # 4 hours of no rain between events
+    bool_end = temp.diff(periods=-1) < -min_gap
+    bool_end.iloc[-1] = True
+
+    bool_start = temp.diff() > min_gap
+    bool_start.iloc[0] = True
+
+    events = pd.DataFrame.from_dict({
+        COL.START: temp[bool_start].to_list(),
+        COL.END: temp[bool_end].to_list(),
+    })
+    return events

+
+
+
+

+[docs]
+def event_number_to_series(events, index):
+    """
+    make a time-series where the value of the event number is paste to the <index>
+
+    Args:
+        events (pandas.DataFrame):
+        index (pandas.DatetimeIndex):
+
+    Returns:
+        pandas.Series:
+    """
+    ts = pd.Series(index=index)
+
+    events_dict = events.to_dict(orient='index')
+    for event_no, event in events_dict.items():
+        ts[event[COL.START]: event[COL.END]] = event_no
+
+    return ts

+
+
+
+########################################################################################################################
+

+[docs]
+def agg_events(events, series, agg='sum'):
+    """
+
+    Args:
+        events (pandas.DataFrame): table of events
+        series (pandas.Series): time-series data
+        agg (str | function): aggregation of time-series
+
+    Returns:
+        numpy.ndarray: result of function of every event
+    """
+    if events.empty:
+        return np.array([])
+
+    if events.index.size > 3500:
+        res = series.groupby(event_number_to_series(events, series.index)).agg(agg).values
+    else:
+        # res = []
+        # for _, event in events.iterrows():
+        #     res.append(series[event[COL.START]:event[COL.END]].agg(agg))
+        res = events.apply(lambda event: series[event[COL.START]:event[COL.END]].agg(agg), axis=1).values
+    return res

+
+
+
+########################################################################################################################
+

+[docs]
+def event_duration(events):
+    """
+    calculate the event duration
+
+    Args:
+        events (pandas.DataFrame): table of events with COL.START and COL.END times
+
+    Returns:
+        pandas.Series: duration of each event
+    """
+    return events[COL.END] - events[COL.START]

+
+
+
+########################################################################################################################
+

+[docs]
+def rain_bar_plot(rain, ax=None, color='#1E88E5', reverse=False, step='post', joinstyle='miter', capstyle='butt'):
+    """
+    Make a standard precipitation/rain plot.
+
+    Args:
+        rain (pandas.Series):
+        ax (matplotlib.axes.Axes):
+        color (str):
+        reverse (bool):
+        step (str):  'mid' 'post' pre'
+
+    Returns:
+        matplotlib.axes.Axes: rain plot
+    """
+    ax = rain.plot(ax=ax, drawstyle=f'steps-{step}', color=color, solid_capstyle=capstyle, solid_joinstyle=joinstyle,
+                   lw=0)
+    ax.fill_between(rain.index, rain.values, 0, step=step, zorder=1000, color=color, capstyle=capstyle,
+                    joinstyle=joinstyle)
+
+    if reverse:
+        # ax.set_ylim(top=0, bottom=rain.max() * 1.1)
+        ax.set_ylim(bottom=0)
+        ax.invert_yaxis()
+    else:
+        ax.set_ylim(bottom=0)
+
+    return ax

+
+
+
+########################################################################################################################
+

+[docs]
+def resample_rain_series(series):
+    """
+
+    Args:
+        series (pandas.Series):
+
+    Returns:
+        tuple[pandas.Series, int]: the resampled series AND the final frequency in minutes
+    """
+    resample_minutes = (
+        (pd.Timedelta(hours=5), 1),
+        (pd.Timedelta(hours=12), 2),
+        (pd.Timedelta(days=1), 5),
+        (pd.Timedelta(days=2), 10),
+        (pd.Timedelta(days=3), 15),
+        (pd.Timedelta(days=4), 20)
+    )
+
+    dur = series.index[-1] - series.index[0]
+    freq = guess_freq(series.index)
+
+    minutes = 1
+    for duration_limit, minutes in resample_minutes:
+        if dur < duration_limit:
+            break
+
+    if freq.delta > pd.Timedelta(minutes=minutes):
+        return series, int(freq / pd.Timedelta(minutes=1))
+
+    # print('resample_rain_series: ', dur, duration_limit, minutes)
+    return series.resample('{}T'.format(minutes)).sum(), minutes

+
+
+ +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/_modules/index.html b/_modules/index.html new file mode 100644 index 0000000..8f7b95b --- /dev/null +++ b/_modules/index.html @@ -0,0 +1,85 @@ + + + + + + + Overview: module code — Intensity Duration Frequency Analyse 0.2.3 documentation + + + + + + + + + + + +
+
+
+
+ +

All modules for which code is available

+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/_sources/README.md.txt b/_sources/README.md.txt new file mode 100644 index 0000000..a480495 --- /dev/null +++ b/_sources/README.md.txt @@ -0,0 +1,210 @@ +© [Institute of Urban Water Management and Landscape Water Engineering](https://www.sww.tugraz.at), [Graz University of Technology](https://www.tugraz.at/home/) and [Markus Pichler](mailto:markus.pichler@tugraz.at) + + +# Intensity duration frequency analysis (based on KOSTRA) + + +[![license](https://img.shields.io/github/license/markuspic/intensity_duration_frequency_analysis.svg?style=flat)](https://github.com/MarkusPic/intensity_duration_frequency_analysis/blob/master/LICENSE) +[![PyPI](https://img.shields.io/pypi/v/idf-analysis.svg)](https://pypi.python.org/pypi/idf-analysis) + +[![PyPI - Downloads](https://img.shields.io/pypi/dd/idf-analysis)](https://pypi.python.org/pypi/idf-analysis) +[![PyPI - Downloads](https://img.shields.io/pypi/dw/idf-analysis)](https://pypi.python.org/pypi/idf-analysis) +[![PyPI - Downloads](https://img.shields.io/pypi/dm/idf-analysis)](https://pypi.python.org/pypi/idf-analysis) + + +Heavy rain as a function of the duration and the return period acc. to [DWA-A 531 (2012)](http://www.dwa.de/dwa/shop/shop.nsf/Produktanzeige?openform&produktid=P-DWAA-8XMUY2). +This program reads the measurement data of the rainfall +and calculates the distribution of the design rainfall as a function of the return period and the duration +for duration steps up to 12 hours (and more) and return period in a range of '0.5a ≤ T_n ≤ 100a'. + +The guideline was used in the application [KOSTRA-DWD](https://www.dwd.de/DE/leistungen/kostra_dwd_rasterwerte/kostra_dwd_rasterwerte.html). + +---- + +> Heavy rainfall data are among the most important planning parameters in water management and hydraulic engineering practice. In urban areas, for example, they are required as initial parameters for the design of rainwater drainage systems and in watercourses for the dimensioning of hydraulic structures. The accuracy of the target values of the corresponding calculation methods and models depends crucially on their accuracy. Their overestimation can lead to considerable additional costs in the structural implementation, their underestimation to an unacceptable, excessive residual risk of failure during the operation of water management and hydraulic engineering facilities. Despite the area-wide availability of heavy rainfall data through "Coordinated Heavy Rainfall Regionalisation Analyses" (KOSTRA), there is still a need for local station analyses, e.g. to evaluate the now extended data series, to evaluate recent developments or to classify local peculiarities in comparison to the KOSTRA data. However, this is only possible without restrictions if the methodological approach recommended in the worksheet is followed. In the DWA-A 531 worksheet, the main features of the ATVA 121 worksheet published in 1985 and of the identical DVWK-R 124 booklet of the DVWK Rules for Water Management "Heavy Rain Evaluation after Return Time and Duration" are retained. The aim of the revision is to take account of current developments without, however, calling into question the standardisation of the procedure for statistical heavy rain analyses which was intended at the time. + +**[DWA-A 531 (2012)](http://www.dwa.de/dwa/shop/shop.nsf/Produktanzeige?openform&produktid=P-DWAA-8XMUY2) Translated with www.DeepL.com/Translator** + +---- + +> An intensity-duration-frequency curve (IDF curve) is a mathematical function that relates the rainfall intensity with its duration and frequency of occurrence. These curves are commonly used in hydrology for flood forecasting and civil engineering for urban drainage design. However, the IDF curves are also analysed in hydrometeorology because of the interest in the time concentration or time-structure of the rainfall. + +**[Wikipedia](https://en.wikipedia.org/wiki/Intensity-duration-frequency_curve)** + +---- + +This package was developed by [Markus Pichler](mailto:markus.pichler@tugraz.at) during his bachelor thesis and finalised it in the course of his employment at the [Institute of Urban Water Management and Landscape Water Engineering](https://www.sww.tugraz.at). + +## Documentation + +Read the docs [here 📖](https://markuspic.github.io/intensity_duration_frequency_analysis). + +## Installation + +This package is written in Python3. (use a version > 3.5) + +``` +pip install idf-analysis +``` + +Add the following tags to the command for special options: + +- ```--user```: To install the package only for the local user account (no admin rights needed) +- ```--upgrade```: To update the package + +### Windows + +You have to install python first (i.e. the original python from the [website](https://www.python.org/downloads/)). + +To use the command-line-tool, it is advisable to add the path to your Python binary to the environment variables [^path1]. +There is also an option during the installation to add python to the PATH automatically. [^path2] + +[^path1]: https://geek-university.com/python/add-python-to-the-windows-path/ +[^path2]: https://datatofish.com/add-python-to-windows-path/ + + +### Linux/Unix + +Python is pre-installed on most operating systems (as you probably knew). + +### Dependencies + +Packages required for this program will be installed with pip during the installation process and can be seen +in the [`requirements.txt`](https://github.com/MarkusPic/intensity_duration_frequency_analysis/blob/master/requirements.txt) file. + +## Usage + +The documentation of the python-API can be found [here](https://markuspic.github.io/intensity_duration_frequency_analysis/api.html). + +## Commandline tool + +The following commands show the usage for Linux/Unix systems. +To use these features on Windows you have to add ```python -m``` before each command. + +To start the script use following commands in the terminal/Prompt + +```idf_analysis``` + +> ```idf_analysis -h``` + +``` +usage: __main__.py [-h] -i INPUT + [-ws {ATV-A_121,DWA-A_531,DWA-A_531_advektiv}] + [-kind {partial,annual}] [-t {>= 0.5 a and <= 100 a}] + [-d {>= 5 min and <= 8640 min}] [-r {>= 0 L/s*ha}] + [-h_N {>= 0 mm}] [--r_720_1] [--plot] [--export_table] + +heavy rain as a function of the duration and the return period acc. to DWA-A +531 (2012) All files will be saved in the same directory of the input file but +in a subfolder called like the inputfile + "_idf_data". Inside this folder a +file called "idf_parameter.yaml"-file will be saved and contains interim- +calculation-results and will be automatically reloaded on the next call. + +optional arguments: + -h, --help show this help message and exit + -i INPUT, --input INPUT + input file with the rain time-series (csv or parquet) + -ws {ATV-A_121,DWA-A_531,DWA-A_531_advektiv}, --worksheet {ATV-A_121,DWA-A_531,DWA-A_531_advektiv} + From which worksheet the recommendations for + calculating the parameters should be taken. + -kind {partial,annual}, --series_kind {partial,annual} + The kind of series used for the calculation. + (Calculation with partial series is more precise and + recommended.) + -t {>= 0.5 a and <= 100 a}, --return_period {>= 0.5 a and <= 100 a} + return period in years (If two of the three variables + (rainfall (height or flow-rate), duration, return + period) are given, the third variable is calculated.) + -d {>= 5 min and <= 8640 min}, --duration {>= 5 min and <= 8640 min} + duration in minutes (If two of the three variables + (rainfall (height or flow-rate), duration, return + period) are given, the third variable is calculated.) + -r {>= 0 L/(s*ha)}, --flow_rate_of_rainfall {>= 0 L/(s*ha)} + rainfall in Liter/(s * ha) (If two of the three + variables (rainfall (height or flow-rate), duration, + return period) are given, the third variable is + calculated.) + -h_N {>= 0 mm}, --height_of_rainfall {>= 0 mm} + rainfall in mm or Liter/m^2 (If two of the three + variables (rainfall (height or flow-rate), duration, + return period) are given, the third variable is + calculated.) + --r_720_1 design rainfall with a duration of 720 minutes (=12 h) + and a return period of 1 year + --plot get a plot of the idf relationship + --export_table get a table of the most frequent used values +``` + +## Example + +[Example Jupyter notebook for the commandline](https://markuspic.github.io/intensity_duration_frequency_analysis/examples/example_commandline) + +[Example Jupyter notebook for the python api](https://markuspic.github.io/intensity_duration_frequency_analysis/examples/example_python_api) + +[Example python skript](https://markuspic.github.io/intensity_duration_frequency_analysis/examples/example_python_api.py) + + +### Example Files + +[Interim Results of the idf analysis](https://markuspic.github.io/intensity_duration_frequency_analysis/examples/ehyd_112086_idf_data/idf_parameters.yaml) + +### Example Plot + +![IDF-Curves-Plot](https://markuspic.github.io/intensity_duration_frequency_analysis/examples/ehyd_112086_idf_data/idf_curves_plot.png) + +### Example IDF table + +[IDF-Table](https://markuspic.github.io/intensity_duration_frequency_analysis/examples/ehyd_112086_idf_data/idf_table_UNIX.csv) + + +| return period in a
duration in min | 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 | +| 180 | 38.01 | 47.13 | 52.46 | 59.18 | 68.30 | 77.42 | 80.36 | 82.76 | 89.48 | 94.81 | 98.60 | +| 270 | 41.01 | 50.60 | 56.21 | 63.28 | 72.87 | 82.46 | 85.55 | 88.07 | 95.14 | 100.75 | 104.73 | +| 360 | 43.29 | 53.23 | 59.04 | 66.37 | 76.31 | 86.25 | 89.45 | 92.06 | 99.39 | 105.20 | 109.33 | +| 450 | 45.14 | 55.36 | 61.33 | 68.87 | 79.08 | 89.30 | 92.59 | 95.28 | 102.81 | 108.79 | 113.03 | +| 600 | 47.64 | 58.23 | 64.43 | 72.23 | 82.82 | 93.41 | 96.82 | 99.61 | 107.42 | 113.61 | 118.01 | +| 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 | + +## Background + +Pseudocode for the parameter calculation. + +``` +For every duration step + calculating event sums + + if using annual event series: # only recommeded for measurements longer than 20 year + converting every max event sum per year to a series + calculating parameters u and w using the gumbel distribution + + elif using partial event series: + converting the n (approximatly 2.72 x measurement duration in years) biggest event sums to a series + calculating parameters u and w using the exponential distribution + +Splitting IDF curve formulation in to several duration ranges +For each duration range: + For each parameter (u and w): + balancing the parameter over all duation steps (in the range) using a given formulation (creating parameters a and b) + # one-folded-logaritmic | two-folded-logarithmic | hyperbolic + +u(D) = f(a_u, b_u, D) +w(D) = f(a_w, b_w, D) + +h(D,Tn) = u(D) + w(D) * ln(Tn) +``` diff --git a/_sources/api.rst.txt b/_sources/api.rst.txt new file mode 100644 index 0000000..10ed9ad --- /dev/null +++ b/_sources/api.rst.txt @@ -0,0 +1,10 @@ +-------------------- +API +-------------------- + +Main Function +-------------------- + +.. autoclass:: idf_analysis.idf_class.IntensityDurationFrequencyAnalyse + :members: + :no-undoc-members: diff --git a/_sources/base_functions.rst.txt b/_sources/base_functions.rst.txt new file mode 100644 index 0000000..9dfff6b --- /dev/null +++ b/_sources/base_functions.rst.txt @@ -0,0 +1,59 @@ +-------------------- +Base Functions +-------------------- + +Calculation Methods +------------------- + +Functions to analyse the precipitations series based on the core method. + + +.. automodule:: idf_analysis.event_series_analysis + :members: + :no-undoc-members: + +.. automodule:: idf_analysis.parameter_formulations + :members: + :no-undoc-members: + +.. autoclass:: idf_analysis.idf_backend.IdfParameters + :members: + :no-undoc-members: + +Input and Output +---------------- + +Function for reading and writing files. + +.. automodule:: idf_analysis.in_out + :members: + :no-undoc-members: + +SWW Utils +--------- + +Functions to help analyse data in a general way. +Most of this function have been developed on the +institute of urban water management at the university of technology Graz. + +.. automodule:: idf_analysis.sww_utils + :members: + :no-undoc-members: + +Converter helper functions +-------------------------- + +Functions to help convert things and units. + +.. automodule:: idf_analysis.little_helpers + :members: + :no-undoc-members: + +Plotting helper functions +------------------------- + +Functions to add or manipulate plotting figures. + +.. automodule:: idf_analysis.plot_helpers + :members: + :no-undoc-members: diff --git a/_sources/examples/example_commandline.ipynb.txt b/_sources/examples/example_commandline.ipynb.txt new file mode 100644 index 0000000..4ae71fd --- /dev/null +++ b/_sources/examples/example_commandline.ipynb.txt @@ -0,0 +1,255 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Example Commandline Use" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "usage: __main__.py [-h] -i INPUT\n", + " [-ws {ATV-A_121,DWA-A_531,DWA-A_531_advektiv}]\n", + " [-kind {partial,annual}] [-t {>= 0.5 a and <= 100 a}]\n", + " [-d {>= 5 min and <= 8640 min}] [-r {>= 0 L/s*ha}]\n", + " [-h_N {>= 0 mm}] [--r_720_1] [--plot] [--export_table]\n", + "\n", + "heavy rain as a function of the duration and the return period acc. to DWA-A\n", + "531 (2012) All files will be saved in the same directory of the input file but\n", + "in a subfolder called like the inputfile + \"_idf_data\". Inside this folder a\n", + "file called \"idf_parameter.yaml\"-file will be saved and contains interim-\n", + "calculation-results and will be automatically reloaded on the next call.\n", + "\n", + "optional arguments:\n", + " -h, --help show this help message and exit\n", + " -i INPUT, --input INPUT\n", + " input file with the rain time-series (csv or parquet)\n", + " -ws {ATV-A_121,DWA-A_531,DWA-A_531_advektiv}, --worksheet {ATV-A_121,DWA-A_531,DWA-A_531_advektiv}\n", + " From which worksheet the recommendations for\n", + " calculating the parameters should be taken.\n", + " -kind {partial,annual}, --series_kind {partial,annual}\n", + " The kind of series used for the calculation.\n", + " (Calculation with partial series is more precise and\n", + " recommended.)\n", + " -t {>= 0.5 a and <= 100 a}, --return_period {>= 0.5 a and <= 100 a}\n", + " return period in years (If two of the three variables\n", + " (rainfall (height or flow-rate), duration, return\n", + " period) are given, the third variable is calculated.)\n", + " -d {>= 5 min and <= 8640 min}, --duration {>= 5 min and <= 8640 min}\n", + " duration in minutes (If two of the three variables\n", + " (rainfall (height or flow-rate), duration, return\n", + " period) are given, the third variable is calculated.)\n", + " -r {>= 0 L/(s*ha)}, --flow_rate_of_rainfall {>= 0 L/(s*ha)}\n", + " rainfall in Liter/(s * ha) (If two of the three\n", + " variables (rainfall (height or flow-rate), duration,\n", + " return period) are given, the third variable is\n", + " calculated.)\n", + " -h_N {>= 0 mm}, --height_of_rainfall {>= 0 mm}\n", + " rainfall in mm or Liter/m^2 (If two of the three\n", + " variables (rainfall (height or flow-rate), duration,\n", + " return period) are given, the third variable is\n", + " calculated.)\n", + " --r_720_1 design rainfall with a duration of 720 minutes (=12 h)\n", + " and a return period of 1 year\n", + " --plot get a plot of the idf relationship\n", + " --export_table get a table of the most frequent used values\n" + ] + } + ], + "source": [ + "! python -m idf_analysis -h" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I used the rain-time-series from ehyd.gv.at with the ID 112086 (Graz-Andritz)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using the subfolder \"ehyd_112086_idf_data\" for the interim- and final-results.\n", + "Found existing interim-results in \"ehyd_112086_idf_data\\idf_parameters.yaml\" and using them for calculations.\n" + ] + } + ], + "source": [ + "! python -m idf_analysis -i ehyd_112086.parquet" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using the subfolder \"ehyd_112086_idf_data\" for the interim- and final-results.\n", + "Found existing interim-results in \"ehyd_112086_idf_data\\idf_parameters.yaml\" and using them for calculations.\n", + "Resultierende Regenhöhe h_N(T_n=1.0a, D=720.0min) = 49.29 mm\n", + "Resultierende Regenspende r_N(T_n=1.0a, D=720.0min) = 11.41 L/(s*ha)\n" + ] + } + ], + "source": [ + "! python -m idf_analysis -i ehyd_112086.parquet --r_720_1" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using the subfolder \"ehyd_112086_idf_data\" for the interim- and final-results.\n", + "Found existing interim-results in \"ehyd_112086_idf_data\\idf_parameters.yaml\" and using them for calculations.\n", + "Resultierende Regenhöhe h_N(T_n=1.0a, D=720.0min) = 49.29 mm\n", + "Resultierende Regenspende r_N(T_n=1.0a, D=720.0min) = 11.41 L/(s*ha)\n" + ] + } + ], + "source": [ + "! python -m idf_analysis -i ehyd_112086.parquet -d 720 -t 1" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using the subfolder \"ehyd_112086_idf_data\" for the interim- and final-results.\n", + "Found existing interim-results in \"ehyd_112086_idf_data\\idf_parameters.yaml\" and using them for calculations.\n", + "The return period is 2.0 years.\n", + "Resultierende Regenhöhe h_N(T_n=2.0a, D=720.0min) = 60.00 mm\n", + "Resultierende Regenspende r_N(T_n=2.0a, D=720.0min) = 13.89 L/(s*ha)\n" + ] + } + ], + "source": [ + "! python -m idf_analysis -i ehyd_112086.parquet -d 720 -h_N 60" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "! python -m idf_analysis -i ehyd_112086.parquet -t 5 -t 15" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using the subfolder \"ehyd_112086_idf_data\" for the interim- and final-results.\n", + "Found existing interim-results in \"ehyd_112086_idf_data\\idf_parameters.yaml\" and using them for calculations.\n", + "Created the IDF-curves-plot and saved the file as \"ehyd_112086_idf_data\\idf__curves_plot.png\".\n" + ] + } + ], + "source": [ + "! python -m idf_analysis -i ehyd_112086.parquet --plot" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using the subfolder \"ehyd_112086_idf_data\" for the interim- and final-results.\n", + "Found existing interim-results in \"ehyd_112086_idf_data\\idf_parameters.yaml\" and using them for calculations.\n", + "return period (a) 1 2 3 5 10 20 25 30 50 75 100\n", + "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\n", + "duration (min) \n", + "5.0 9.39 10.97 11.89 13.04 14.61 16.19 16.69 17.11 18.26 19.18 19.83\n", + "10.0 15.15 17.62 19.06 20.88 23.35 25.82 26.62 27.27 29.09 30.54 31.56\n", + "15.0 19.03 22.25 24.13 26.51 29.72 32.94 33.98 34.83 37.20 39.08 40.42\n", + "20.0 21.83 25.71 27.99 30.85 34.73 38.62 39.87 40.89 43.75 46.02 47.63\n", + "30.0 25.60 30.66 33.62 37.35 42.41 47.47 49.10 50.43 54.16 57.12 59.22\n", + "45.0 28.92 35.51 39.37 44.23 50.83 57.42 59.54 61.28 66.14 69.99 72.73\n", + "60.0 30.93 38.89 43.54 49.40 57.36 65.31 67.88 69.97 75.83 80.49 83.79\n", + "90.0 33.37 41.74 46.64 52.80 61.17 69.54 72.23 74.43 80.60 85.49 88.96\n", + "180.0 38.01 47.13 52.46 59.18 68.30 77.42 80.36 82.76 89.48 94.81 98.60\n", + "270.0 41.01 50.60 56.21 63.28 72.87 82.46 85.55 88.07 95.14 100.75 104.73\n", + "360.0 43.29 53.23 59.04 66.37 76.31 86.25 89.45 92.06 99.39 105.20 109.33\n", + "450.0 45.14 55.36 61.33 68.87 79.08 89.30 92.59 95.28 102.81 108.79 113.03\n", + "600.0 47.64 58.23 64.43 72.23 82.82 93.41 96.82 99.61 107.42 113.61 118.01\n", + "720.0 49.29 60.13 66.47 74.45 85.29 96.12 99.61 102.46 110.44 116.78 121.28\n", + "1080.0 54.41 64.97 71.15 78.94 89.50 100.06 103.46 106.24 114.02 120.20 124.58\n", + "1440.0 58.02 67.72 73.39 80.54 90.24 99.93 103.05 105.61 112.75 118.42 122.45\n", + "2880.0 66.70 77.41 83.68 91.57 102.29 113.00 116.45 119.26 127.16 133.42 137.87\n", + "4320.0 71.93 85.72 93.78 103.95 117.73 131.52 135.96 139.58 149.75 157.81 163.53\n", + "5760.0 78.95 95.65 105.42 117.72 134.43 151.13 156.50 160.89 173.20 182.97 189.90\n", + "7200.0 83.53 101.38 111.82 124.98 142.83 160.68 166.43 171.12 184.28 194.72 202.13\n", + "8640.0 85.38 104.95 116.40 130.82 150.38 169.95 176.25 181.40 195.82 207.27 215.39\n", + "Created the IDF-curves-plot and saved the file as \"ehyd_112086_idf_data\\idf_table.csv\".\n" + ] + } + ], + "source": [ + "! python -m idf_analysis -i ehyd_112086.parquet --export_table" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} \ No newline at end of file diff --git a/_sources/examples/example_heavy_rainfall_index.ipynb.txt b/_sources/examples/example_heavy_rainfall_index.ipynb.txt new file mode 100644 index 0000000..d80d0c2 --- /dev/null +++ b/_sources/examples/example_heavy_rainfall_index.ipynb.txt @@ -0,0 +1,442 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Example for Heavy Rainfall Index" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "Based on the IntensityDurationFrequencyAnalysis, the HeavyRainIndexAnalysis enables the creation of location-dependent heavy rain indices according to the methods of Schmitt, Mudersbach and also those of Krüger and Pfister.\n", + "Furthermore, the possibility of creating heavy rain index curves according to the Krüger and Pfister method was included and implemented for the other two methods. Thus, heavy rainfall index curves can be compared with each other. Furthermore, it is possible to assign individual rain events to a heavy rain index using existing rain data." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib.colors import ListedColormap\n", + "from idf_analysis.definitions import METHOD, SERIES, COL\n", + "from idf_analysis.little_helpers import minutes_readable\n", + "from idf_analysis.sww_utils import rain_events, event_duration, agg_events\n", + "from idf_analysis.heavy_rainfall_index import HeavyRainfallIndexAnalyse\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from os import path\n", + "\n", + "%matplotlib inline\n", + "plt.style.use('fast')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Implemented Methods for the HeavyRainIndexAnalysis: \n", + "SCHMITT = 'Schmitt' \n", + "KRUEGER_PFISTER = 'KruegerPfister' \n", + "MUDERSBACH = 'Mudersbach'" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "hri = HeavyRainfallIndexAnalyse(method=HeavyRainfallIndexAnalyse.METHODS.KRUEGER_PFISTER,\n", + " series_kind=SERIES.PARTIAL, worksheet=METHOD.KOSTRA, extended_durations=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "cmap = ListedColormap([(1,1,1)] + list(hri.indices_color.values()))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_parquet('ehyd_112086.parquet')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "output_directory = 'Ergebnisse'" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "hri.set_series(data['precipitation'])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "hri.auto_save_parameters(path.join(output_directory, 'idf_parameters.yaml'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Heavy rainfall index-matrix is created with regard to the individual procedures for SRI generation" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": "", + "text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
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\n" + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hri.interim_sri_table().style.background_gradient(cmap=cmap, vmin=1, vmax=12)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An auxiliary table must be generated for the creation of the heavy rain index-curves. Here, the rainfall heights are shown depending on the duration and the heavy rain index." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": "", + "text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
SRI123456789101112
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\n" + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hri.result_sri_table().style.background_gradient(cmap=cmap, vmin=0, vmax=700).format(\"{:.1f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using heavy rainfall index curves, the necessary rainfall heights can be read off depending on the respective index." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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+ }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hri.method = hri.METHODS.KRUEGER_PFISTER\n", + "fig, ax = hri.result_sri_figure()\n", + "ax.legend(loc='upper left')\n", + "ax.grid(color='grey', linestyle='-', linewidth=0.3)\n", + "ax.set_ylim(0, 230)\n", + "old_labels = ax.get_xticklabels()\n", + "old_labels[2] = ''\n", + "old_labels[5] = ''\n", + "old_labels[19] = ''\n", + "ax.set_xticklabels(old_labels)\n", + "handles, labels = ax.get_legend_handles_labels()\n", + "ax.legend(handles[::-1], labels[::-1], title='SRI', loc='upper left')\n", + "plt.savefig(\"kruegerpfister.svg\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Heavy rain index allocation of a specific rain event" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "calculating rainfall_sum data-frame: 100%|██████████| 21/21 [00:00<00:00, 163.22it/s]\n", + "calculating return_periods data-frame: 100%|██████████| 21/21 [00:00<00:00, 58.97it/s]\n" + ] + }, + { + "data": { + "text/plain": " start end duration rain_sum \\\n40 2008-07-20 17:43:00 2008-07-21 08:57:00 0 days 15:14:00 28.0 \n36 2008-06-23 19:50:00 2008-06-24 19:48:00 0 days 23:58:00 35.8 \n96 2009-08-27 19:52:00 2009-08-29 13:25:00 1 days 17:33:00 55.1 \n88 2009-07-18 08:55:00 2009-07-18 13:19:00 0 days 04:24:00 58.0 \n95 2009-08-21 19:49:00 2009-08-22 20:46:00 1 days 00:57:00 64.2 \n433 2016-05-30 12:38:00 2016-06-06 12:55:00 7 days 00:17:00 66.4 \n97 2009-09-04 00:31:00 2009-09-05 01:32:00 1 days 01:01:00 69.8 \n193 2011-07-27 20:37:00 2011-08-04 07:45:00 7 days 11:08:00 70.5 \n391 2015-07-08 11:50:00 2015-07-09 00:26:00 0 days 12:36:00 77.8 \n331 2014-05-07 16:49:00 2014-05-18 20:03:00 11 days 03:14:00 109.9 \n302 2013-08-24 01:33:00 2013-08-29 08:20:00 5 days 06:47:00 114.1 \n39 2008-07-12 22:10:00 2008-07-18 14:55:00 5 days 16:45:00 122.8 \n244 2012-07-09 18:49:00 2012-07-16 05:43:00 6 days 10:54:00 123.1 \n441 2016-07-21 22:28:00 2016-07-28 14:14:00 6 days 15:46:00 136.6 \n287 2013-05-02 02:45:00 2013-05-07 21:38:00 5 days 18:53:00 148.2 \n\n last_event max_return_period max_return_period_duration \n40 2 days 02:48:00 2.831020 5760.0 \n36 2 days 21:52:00 5.442798 15.0 \n96 4 days 23:06:00 19.718725 20.0 \n88 2 days 01:56:00 3.379539 240.0 \n95 4 days 03:04:00 3.975735 20.0 \n433 4 days 17:43:00 6.793788 5.0 \n97 5 days 11:06:00 2.605674 1080.0 \n193 3 days 05:28:00 12.732452 20.0 \n391 8 days 13:52:00 5.981540 720.0 \n331 4 days 03:29:00 2.798825 4320.0 \n302 9 days 12:51:00 3.543855 2880.0 \n39 2 days 11:12:00 6.296355 120.0 \n244 5 days 23:07:00 3.720530 8640.0 \n441 5 days 00:03:00 4.663786 8640.0 \n287 9 days 05:06:00 32.602489 2880.0 ", + "text/html": "
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startenddurationrain_sumlast_eventmax_return_periodmax_return_period_duration
402008-07-20 17:43:002008-07-21 08:57:000 days 15:14:0028.02 days 02:48:002.8310205760.0
362008-06-23 19:50:002008-06-24 19:48:000 days 23:58:0035.82 days 21:52:005.44279815.0
962009-08-27 19:52:002009-08-29 13:25:001 days 17:33:0055.14 days 23:06:0019.71872520.0
882009-07-18 08:55:002009-07-18 13:19:000 days 04:24:0058.02 days 01:56:003.379539240.0
952009-08-21 19:49:002009-08-22 20:46:001 days 00:57:0064.24 days 03:04:003.97573520.0
4332016-05-30 12:38:002016-06-06 12:55:007 days 00:17:0066.44 days 17:43:006.7937885.0
972009-09-04 00:31:002009-09-05 01:32:001 days 01:01:0069.85 days 11:06:002.6056741080.0
1932011-07-27 20:37:002011-08-04 07:45:007 days 11:08:0070.53 days 05:28:0012.73245220.0
3912015-07-08 11:50:002015-07-09 00:26:000 days 12:36:0077.88 days 13:52:005.981540720.0
3312014-05-07 16:49:002014-05-18 20:03:0011 days 03:14:00109.94 days 03:29:002.7988254320.0
3022013-08-24 01:33:002013-08-29 08:20:005 days 06:47:00114.19 days 12:51:003.5438552880.0
392008-07-12 22:10:002008-07-18 14:55:005 days 16:45:00122.82 days 11:12:006.296355120.0
2442012-07-09 18:49:002012-07-16 05:43:006 days 10:54:00123.15 days 23:07:003.7205308640.0
4412016-07-21 22:28:002016-07-28 14:14:006 days 15:46:00136.65 days 00:03:004.6637868640.0
2872013-05-02 02:45:002013-05-07 21:38:005 days 18:53:00148.29 days 05:06:0032.6024892880.0
\n
" + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "events = rain_events(hri.series, min_gap=pd.Timedelta(days=2))\n", + "events[COL.DUR] = event_duration(events)\n", + "events[COL.LP] = agg_events(events, hri.series, 'sum').round(1)\n", + "events[COL.LAST] = events[COL.START] - events[COL.END].shift()\n", + "\n", + "hri.add_max_return_periods_to_events(events)\n", + "\n", + "events = events[events[COL.LP] > 10]\n", + "events = events[events[COL.MAX_PERIOD] > 2]\n", + "\n", + "events.sort_values(COL.LP)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "hri.add_max_return_periods_to_events(hri.rain_events)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": " start end duration rain_sum \\\n798 2013-05-07 20:57:00 2013-05-07 21:38:00 0 days 00:41:00 2.0 \n797 2013-05-05 20:46:00 2013-05-07 12:41:00 1 days 15:55:00 119.6 \n289 2009-08-28 23:41:00 2009-08-29 00:42:00 0 days 01:01:00 49.3 \n566 2011-08-03 19:52:00 2011-08-04 07:45:00 0 days 11:53:00 54.7 \n799 2013-05-10 17:55:00 2013-05-10 22:21:00 0 days 04:26:00 3.9 \n\n last_event max_return_period max_return_period_duration \n798 0 days 08:16:00 32.602489 2880.0 \n797 0 days 17:29:00 30.585304 2880.0 \n289 1 days 03:44:00 19.718725 20.0 \n566 1 days 23:09:00 12.732452 20.0 \n799 2 days 20:17:00 7.412151 8640.0 ", + "text/html": "
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startenddurationrain_sumlast_eventmax_return_periodmax_return_period_duration
7982013-05-07 20:57:002013-05-07 21:38:000 days 00:41:002.00 days 08:16:0032.6024892880.0
7972013-05-05 20:46:002013-05-07 12:41:001 days 15:55:00119.60 days 17:29:0030.5853042880.0
2892009-08-28 23:41:002009-08-29 00:42:000 days 01:01:0049.31 days 03:44:0019.71872520.0
5662011-08-03 19:52:002011-08-04 07:45:000 days 11:53:0054.71 days 23:09:0012.73245220.0
7992013-05-10 17:55:002013-05-10 22:21:000 days 04:26:003.92 days 20:17:007.4121518640.0
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" + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hri.rain_events.sort_values('max_return_period', ascending=False).head()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/markus/.local/lib/python3.8/site-packages/pandas/core/series.py:1056: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " cacher_needs_updating = self._check_is_chained_assignment_possible()\n" + ] + }, + { + "data": { + "text/plain": "start 2013-05-06 20:00:00\nend 2013-05-07 12:41:00\nduration 1 days 15:55:00\nrain_sum 119.6\nlast_event 0 days 17:29:00\nmax_return_period 30.585304\nmax_return_period_duration 2880.0\nName: 797, dtype: object" + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "event = hri.rain_events.loc[797]\n", + "\n", + "event[COL.START] = pd.to_datetime('2013-05-06 20:00:00')\n", + "event" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "calculating rainfall_sum data-frame: 100%|██████████| 21/21 [00:00<00:00, 171.82it/s]\n" + ] + } + ], + "source": [ + "rainfall_sum_frame = hri.rainfall_sum_frame[event[COL.START]:event[COL.END]]\n", + "return_periods_frame = hri.return_periods_frame[event[COL.START]:event[COL.END]]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": "", + "text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 max. Regensumme
5 min9.9
10 min16.3
15 min21.9
20 min28.2
30 min40.7
45 min46.3
60 min57.0
1.5 h69.6
2 h74.3
3 h76.5
4 h78.7
6 h89.6
9 h92.8
12 h93.0
18 h96.4
1 d98.2
2 d119.6
3 d136.0
4 d136.0
5 d143.2
6 d146.2
\n" + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rainfall_sum_frame.\\\n", + " max().\\\n", + " rename('max. Regensumme').\\\n", + " to_frame().\\\n", + " rename(minutes_readable).\\\n", + " style.bar(vmin=0, vmax=100).format(\"{:.1f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": "", + "text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 max. Wiederkehrperiode
5 min1.4
10 min1.5
15 min1.9
20 min3.2
30 min7.7
45 min6.0
60 min9.3
1.5 h19.1
2 h21.6
3 h17.9
4 h16.4
6 h24.6
9 h20.8
12 h16.2
18 h15.8
1 d17.7
2 d30.6
3 d25.0
4 d10.7
5 d10.1
6 d8.6
\n" + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "return_periods_frame.\\\n", + " max().\\\n", + " rename('max. Wiederkehrperiode').\\\n", + " to_frame().\\\n", + " rename(minutes_readable).\\\n", + " style.bar(vmin=0, vmax=100).format(\"{:.1f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "sri_table_event = pd.DataFrame(index=hri.duration_steps)\n", + "\n", + "hri.method = hri.METHODS.KRUEGER_PFISTER\n", + "sri_table_event[hri.METHODS.KRUEGER_PFISTER] = hri.get_event_sri_max(event[COL.START], event[COL.END])\n", + "\n", + "hri.method = hri.METHODS.MUDERSBACH\n", + "sri_table_event[hri.METHODS.MUDERSBACH] = hri.get_event_sri_max(event[COL.START], event[COL.END])\n", + "\n", + "hri.method = hri.METHODS.SCHMITT\n", + "sri_table_event[hri.METHODS.SCHMITT] = hri.get_event_sri_max(event[COL.START], event[COL.END])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Specific rain event with allocation of heavy rain indices depending on the method and duration. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": "", + "text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 KruegerPfisterMudersbachSchmittmax. Wiederkehrperiodemax. Regensumme
5 min1111.49.9
10 min1211.516.3
15 min1211.921.9
20 min1323.228.2
30 min2437.740.7
45 min2436.046.3
60 min2539.357.0
1.5 h36419.169.6
2 h47421.674.3
3 h46417.976.5
4 h46416.478.7
6 h47424.689.6
9 h47420.892.8
12 h47416.293.0
18 h47415.896.4
1 d57417.798.2
2 d68630.6119.6
3 d78525.0136.0
4 d57410.7136.0
5 d57410.1143.2
6 d5738.6146.2
\n" + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cmap = ListedColormap([(1,1,1)] + list(hri.indices_color.values()))\n", + "_df = sri_table_event.astype(int).copy()\n", + "_df['max. Wiederkehrperiode'] = return_periods_frame.max()\n", + "_df['max. Regensumme'] = rainfall_sum_frame.max()\n", + "_df.rename(minutes_readable).style.\\\n", + " background_gradient(subset=[hri.METHODS.SCHMITT,\n", + " hri.METHODS.KRUEGER_PFISTER,\n", + " hri.METHODS.MUDERSBACH], cmap=cmap, vmin=0, vmax=12).\\\n", + " bar(subset=['max. Wiederkehrperiode', 'max. Regensumme'], vmin=0, vmax=100).format(\"{:.1f}\", subset=['max. Wiederkehrperiode', 'max. Regensumme'])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/_sources/examples/example_python_api.ipynb.txt b/_sources/examples/example_python_api.ipynb.txt new file mode 100644 index 0000000..c320370 --- /dev/null +++ b/_sources/examples/example_python_api.ipynb.txt @@ -0,0 +1,534 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "from idf_analysis.idf_class import IntensityDurationFrequencyAnalyse\n", + "from idf_analysis.definitions import *\n", + "import pandas as pd\n", + "from os import path\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "plt.style.use('bmh')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Intensity Duration Frequency Analyse" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parameter\n", + "\n", + "**series_kind**:\n", + "\n", + "`SERIES.PARTIAL` = Partielle Serie (partial duration series, PDS) (peak over threshold, POT)\n", + "\n", + "`SERIES.ANNUAL` = Jährliche Serie (annual maximum series, AMS)\n", + "\n", + "**worksheet**:\n", + "\n", + "`METHOD.KOSTRA`:\n", + "- DWA-A 531\n", + "- KOSTRA - empfohlen\n", + "- Stützstellen: 60 min und 12 h\n", + "\n", + "`METHOD.CONVECTIVE_ADVECTIVE`:\n", + "- DWA-A 531\n", + "- Unterscheidung in überwiegend konvektiv und advektiv verursachte Starkregen\n", + "- Stützstellen: 3 h und 24 h\n", + "\n", + "`METHOD.ATV`:\n", + "- ATV-A 121\n", + "- Stützstellen: 3 h und 48 h\n", + "\n", + "**extended_durations** = Inkludiert die Dauerstufen `[0.75d, 1d, 2d, 3d, 4d, 5d, 6d]` in der Analyse (in d=Tage)\n", + "\n", + "Standardmäßig berechnete Dauerstufen `[5m, 10m, 15m, 20m, 30m, 45m, 60m, 1.5h, 3h, 4.5h, 6h, 7.5h, 10h, 12h]`" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "idf = IntensityDurationFrequencyAnalyse(series_kind=SERIES.PARTIAL, worksheet=METHOD.KOSTRA, extended_durations=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I used the rain-time-series from ehyd.gv.at with the ID 112086 (Graz-Andritz)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "data = pd.read_parquet('ehyd_112086.parquet')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "output_directory = 'ehyd_112086_idf_data'" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": " precipitation\ndatetime \n2007-09-18 11:09:00 0.1\n2007-09-18 11:12:00 0.1\n2007-09-18 11:13:00 0.1\n2007-09-18 11:14:00 0.1\n2007-09-18 11:20:00 0.1", + "text/html": "
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precipitation
datetime
2007-09-18 11:09:000.1
2007-09-18 11:12:000.1
2007-09-18 11:13:000.1
2007-09-18 11:14:000.1
2007-09-18 11:20:000.1
\n
" + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": " precipitation\ndatetime \n2016-12-28 22:24:00 0.006\n2016-12-28 22:25:00 0.092\n2016-12-28 22:26:00 0.006\n2016-12-28 22:45:00 0.090\n2016-12-28 22:46:00 0.006", + "text/html": "
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precipitation
datetime
2016-12-28 22:24:000.006
2016-12-28 22:25:000.092
2016-12-28 22:26:000.006
2016-12-28 22:45:000.090
2016-12-28 22:46:000.006
\n
" + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "idf.set_series(data['precipitation'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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.\n", + "Auserdem enthalten diese Zwischenergebnisse die Parameter, die zur Berechnung der Regenhöhe und Regenspende benötigt werden.\n", + "Hier sind bereist die Berechnungsverfahren und Stückpunkte laut dem gewählten `worksheet` berücksichtigt." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "idf.auto_save_parameters(path.join(output_directory, 'idf_parameters.yaml'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Abgerufen können diese Zwischenergebnisse mit:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idf.parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Berechnungen" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": "19.031596336052708" + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idf.depth_of_rainfall(duration=15, return_period=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Resultierende Regenhöhe h_N(T_n=1.0a, D=15.0min) = 19.03 mm\n" + ] + } + ], + "source": [ + "print('Resultierende Regenhöhe h_N(T_n={t:0.1f}a, D={d:0.1f}min) = {h:0.2f} mm'\n", + " ''.format(t=1, d=15, h=idf.depth_of_rainfall(15, 1)))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": "211.46218151169674" + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idf.rain_flow_rate(duration=15, return_period=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Resultierende Regenspende r_N(T_n=1.0a, D=15.0min) = 211.46 L/(s*ha)\n" + ] + } + ], + "source": [ + "print('Resultierende Regenspende r_N(T_n={t:0.1f}a, D={d:0.1f}min) = {r:0.2f} L/(s*ha)'\n", + " ''.format(t=1, d=15, r=idf.rain_flow_rate(15, 1)))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": "11.410836729727" + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idf.r_720_1()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": "0.1430180144131331" + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idf.get_return_period(height_of_rainfall=10, duration=15)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": "5.433080747189968" + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idf.get_duration(height_of_rainfall=10, return_period=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": " 1 2 3 5 10 20 25 30 50 \\\n5 9.39 10.97 11.89 13.04 14.61 16.19 16.69 17.11 18.26 \n10 15.15 17.62 19.06 20.88 23.35 25.82 26.62 27.27 29.09 \n15 19.03 22.25 24.13 26.51 29.72 32.94 33.98 34.83 37.20 \n20 21.83 25.71 27.99 30.85 34.73 38.62 39.87 40.89 43.75 \n30 25.60 30.66 33.62 37.35 42.41 47.47 49.10 50.43 54.16 \n45 28.92 35.51 39.37 44.23 50.83 57.42 59.54 61.28 66.14 \n60 30.93 38.89 43.54 49.40 57.36 65.31 67.88 69.97 75.83 \n90 33.37 41.74 46.64 52.80 61.17 69.54 72.23 74.43 80.60 \n120 35.22 43.90 48.97 55.36 64.03 72.70 75.49 77.78 84.17 \n180 38.01 47.13 52.46 59.18 68.30 77.42 80.36 82.76 89.48 \n240 40.12 49.57 55.10 62.06 71.51 80.97 84.01 86.49 93.46 \n360 43.29 53.23 59.04 66.37 76.31 86.25 89.45 92.06 99.39 \n540 46.71 57.16 63.27 70.98 81.43 91.89 95.25 98.00 105.71 \n720 49.29 60.13 66.47 74.45 85.29 96.12 99.61 102.46 110.44 \n1080 54.41 64.97 71.15 78.94 89.50 100.06 103.46 106.24 114.02 \n1440 58.02 67.72 73.39 80.54 90.24 99.93 103.05 105.61 112.75 \n2880 66.70 77.41 83.68 91.57 102.29 113.00 116.45 119.26 127.16 \n4320 71.93 85.72 93.78 103.95 117.73 131.52 135.96 139.58 149.75 \n5760 78.95 95.65 105.42 117.72 134.43 151.13 156.50 160.89 173.20 \n7200 83.53 101.38 111.82 124.98 142.83 160.68 166.43 171.12 184.28 \n8640 85.38 104.95 116.40 130.82 150.38 169.95 176.25 181.40 195.82 \n\n 75 100 \n5 19.18 19.83 \n10 30.54 31.56 \n15 39.08 40.42 \n20 46.02 47.63 \n30 57.12 59.22 \n45 69.99 72.73 \n60 80.49 83.79 \n90 85.49 88.96 \n120 89.24 92.84 \n180 94.81 98.60 \n240 98.99 102.91 \n360 105.20 109.33 \n540 111.82 116.16 \n720 116.78 121.28 \n1080 120.20 124.58 \n1440 118.42 122.45 \n2880 133.42 137.87 \n4320 157.81 163.53 \n5760 182.97 189.90 \n7200 194.72 202.13 \n8640 207.27 215.39 ", + "text/html": "
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1235102025305075100
59.3910.9711.8913.0414.6116.1916.6917.1118.2619.1819.83
1015.1517.6219.0620.8823.3525.8226.6227.2729.0930.5431.56
1519.0322.2524.1326.5129.7232.9433.9834.8337.2039.0840.42
2021.8325.7127.9930.8534.7338.6239.8740.8943.7546.0247.63
3025.6030.6633.6237.3542.4147.4749.1050.4354.1657.1259.22
4528.9235.5139.3744.2350.8357.4259.5461.2866.1469.9972.73
6030.9338.8943.5449.4057.3665.3167.8869.9775.8380.4983.79
9033.3741.7446.6452.8061.1769.5472.2374.4380.6085.4988.96
12035.2243.9048.9755.3664.0372.7075.4977.7884.1789.2492.84
18038.0147.1352.4659.1868.3077.4280.3682.7689.4894.8198.60
24040.1249.5755.1062.0671.5180.9784.0186.4993.4698.99102.91
36043.2953.2359.0466.3776.3186.2589.4592.0699.39105.20109.33
54046.7157.1663.2770.9881.4391.8995.2598.00105.71111.82116.16
72049.2960.1366.4774.4585.2996.1299.61102.46110.44116.78121.28
108054.4164.9771.1578.9489.50100.06103.46106.24114.02120.20124.58
144058.0267.7273.3980.5490.2499.93103.05105.61112.75118.42122.45
288066.7077.4183.6891.57102.29113.00116.45119.26127.16133.42137.87
432071.9385.7293.78103.95117.73131.52135.96139.58149.75157.81163.53
576078.9595.65105.42117.72134.43151.13156.50160.89173.20182.97189.90
720083.53101.38111.82124.98142.83160.68166.43171.12184.28194.72202.13
864085.38104.95116.40130.82150.38169.95176.25181.40195.82207.27215.39
\n
" + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idf.result_table().round(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": "return period (a) 1 2 3 5 10 20 25 \\\nfrequency (1/a) 1.000 0.500 0.333 0.200 0.100 0.050 0.040 \nduration (min) \n5 9.39 10.97 11.89 13.04 14.61 16.19 16.69 \n10 15.15 17.62 19.06 20.88 23.35 25.82 26.62 \n15 19.03 22.25 24.13 26.51 29.72 32.94 33.98 \n20 21.83 25.71 27.99 30.85 34.73 38.62 39.87 \n30 25.60 30.66 33.62 37.35 42.41 47.47 49.10 \n45 28.92 35.51 39.37 44.23 50.83 57.42 59.54 \n60 30.93 38.89 43.54 49.40 57.36 65.31 67.88 \n90 33.37 41.74 46.64 52.80 61.17 69.54 72.23 \n120 35.22 43.90 48.97 55.36 64.03 72.70 75.49 \n180 38.01 47.13 52.46 59.18 68.30 77.42 80.36 \n240 40.12 49.57 55.10 62.06 71.51 80.97 84.01 \n360 43.29 53.23 59.04 66.37 76.31 86.25 89.45 \n540 46.71 57.16 63.27 70.98 81.43 91.89 95.25 \n720 49.29 60.13 66.47 74.45 85.29 96.12 99.61 \n1080 54.41 64.97 71.15 78.94 89.50 100.06 103.46 \n1440 58.02 67.72 73.39 80.54 90.24 99.93 103.05 \n2880 66.70 77.41 83.68 91.57 102.29 113.00 116.45 \n4320 71.93 85.72 93.78 103.95 117.73 131.52 135.96 \n5760 78.95 95.65 105.42 117.72 134.43 151.13 156.50 \n7200 83.53 101.38 111.82 124.98 142.83 160.68 166.43 \n8640 85.38 104.95 116.40 130.82 150.38 169.95 176.25 \n\nreturn period (a) 30 50 75 100 \nfrequency (1/a) 0.033 0.020 0.013 0.010 \nduration (min) \n5 17.11 18.26 19.18 19.83 \n10 27.27 29.09 30.54 31.56 \n15 34.83 37.20 39.08 40.42 \n20 40.89 43.75 46.02 47.63 \n30 50.43 54.16 57.12 59.22 \n45 61.28 66.14 69.99 72.73 \n60 69.97 75.83 80.49 83.79 \n90 74.43 80.60 85.49 88.96 \n120 77.78 84.17 89.24 92.84 \n180 82.76 89.48 94.81 98.60 \n240 86.49 93.46 98.99 102.91 \n360 92.06 99.39 105.20 109.33 \n540 98.00 105.71 111.82 116.16 \n720 102.46 110.44 116.78 121.28 \n1080 106.24 114.02 120.20 124.58 \n1440 105.61 112.75 118.42 122.45 \n2880 119.26 127.16 133.42 137.87 \n4320 139.58 149.75 157.81 163.53 \n5760 160.89 173.20 182.97 189.90 \n7200 171.12 184.28 194.72 202.13 \n8640 181.40 195.82 207.27 215.39 ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
return period (a)1235102025305075100
frequency (1/a)1.0000.5000.3330.2000.1000.0500.0400.0330.0200.0130.010
duration (min)
59.3910.9711.8913.0414.6116.1916.6917.1118.2619.1819.83
1015.1517.6219.0620.8823.3525.8226.6227.2729.0930.5431.56
1519.0322.2524.1326.5129.7232.9433.9834.8337.2039.0840.42
2021.8325.7127.9930.8534.7338.6239.8740.8943.7546.0247.63
3025.6030.6633.6237.3542.4147.4749.1050.4354.1657.1259.22
4528.9235.5139.3744.2350.8357.4259.5461.2866.1469.9972.73
6030.9338.8943.5449.4057.3665.3167.8869.9775.8380.4983.79
9033.3741.7446.6452.8061.1769.5472.2374.4380.6085.4988.96
12035.2243.9048.9755.3664.0372.7075.4977.7884.1789.2492.84
18038.0147.1352.4659.1868.3077.4280.3682.7689.4894.8198.60
24040.1249.5755.1062.0671.5180.9784.0186.4993.4698.99102.91
36043.2953.2359.0466.3776.3186.2589.4592.0699.39105.20109.33
54046.7157.1663.2770.9881.4391.8995.2598.00105.71111.82116.16
72049.2960.1366.4774.4585.2996.1299.61102.46110.44116.78121.28
108054.4164.9771.1578.9489.50100.06103.46106.24114.02120.20124.58
144058.0267.7273.3980.5490.2499.93103.05105.61112.75118.42122.45
288066.7077.4183.6891.57102.29113.00116.45119.26127.16133.42137.87
432071.9385.7293.78103.95117.73131.52135.96139.58149.75157.81163.53
576078.9595.65105.42117.72134.43151.13156.50160.89173.20182.97189.90
720083.53101.38111.82124.98142.83160.68166.43171.12184.28194.72202.13
864085.38104.95116.40130.82150.38169.95176.25181.40195.82207.27215.39
\n
" + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idf.result_table(add_names=True).round(2)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "To save the table as a csv:" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "idf.result_table(add_names=True).round(2).to_csv(path.join(output_directory, 'idf_table_UNIX.csv'), sep=',', decimal='.', float_format='%0.2f')" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "return period (a) 1 2 3 5 10 20 25 30 50 75 100\n", + "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\n", + "duration (min) \n", + "5 9.39 10.97 11.89 13.04 14.61 16.19 16.69 17.11 18.26 19.18 19.83\n", + "10 15.15 17.62 19.06 20.88 23.35 25.82 26.62 27.27 29.09 30.54 31.56\n", + "15 19.03 22.25 24.13 26.51 29.72 32.94 33.98 34.83 37.20 39.08 40.42\n", + "20 21.83 25.71 27.99 30.85 34.73 38.62 39.87 40.89 43.75 46.02 47.63\n", + "30 25.60 30.66 33.62 37.35 42.41 47.47 49.10 50.43 54.16 57.12 59.22\n", + "45 28.92 35.51 39.37 44.23 50.83 57.42 59.54 61.28 66.14 69.99 72.73\n", + "60 30.93 38.89 43.54 49.40 57.36 65.31 67.88 69.97 75.83 80.49 83.79\n", + "90 33.37 41.74 46.64 52.80 61.17 69.54 72.23 74.43 80.60 85.49 88.96\n", + "120 35.22 43.90 48.97 55.36 64.03 72.70 75.49 77.78 84.17 89.24 92.84\n", + "180 38.01 47.13 52.46 59.18 68.30 77.42 80.36 82.76 89.48 94.81 98.60\n", + "240 40.12 49.57 55.10 62.06 71.51 80.97 84.01 86.49 93.46 98.99 102.91\n", + "360 43.29 53.23 59.04 66.37 76.31 86.25 89.45 92.06 99.39 105.20 109.33\n", + "540 46.71 57.16 63.27 70.98 81.43 91.89 95.25 98.00 105.71 111.82 116.16\n", + "720 49.29 60.13 66.47 74.45 85.29 96.12 99.61 102.46 110.44 116.78 121.28\n", + "1080 54.41 64.97 71.15 78.94 89.50 100.06 103.46 106.24 114.02 120.20 124.58\n", + "1440 58.02 67.72 73.39 80.54 90.24 99.93 103.05 105.61 112.75 118.42 122.45\n", + "2880 66.70 77.41 83.68 91.57 102.29 113.00 116.45 119.26 127.16 133.42 137.87\n", + "4320 71.93 85.72 93.78 103.95 117.73 131.52 135.96 139.58 149.75 157.81 163.53\n", + "5760 78.95 95.65 105.42 117.72 134.43 151.13 156.50 160.89 173.20 182.97 189.90\n", + "7200 83.53 101.38 111.82 124.98 142.83 160.68 166.43 171.12 184.28 194.72 202.13\n", + "8640 85.38 104.95 116.40 130.82 150.38 169.95 176.25 181.40 195.82 207.27 215.39\n" + ] + } + ], + "source": [ + "print(idf.result_table(add_names=True).round(2).to_string())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To create a color plot of the IDF curves:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = idf.result_figure(color=True, add_interim=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To create a black/white plot of the IDF curves:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = idf.result_figure(color=False, add_interim=True)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/_sources/examples/example_python_api_extended.ipynb.txt b/_sources/examples/example_python_api_extended.ipynb.txt new file mode 100644 index 0000000..fc5cdf5 --- /dev/null +++ b/_sources/examples/example_python_api_extended.ipynb.txt @@ -0,0 +1,288 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from idf_analysis.idf_class import IntensityDurationFrequencyAnalyse\n", + "from idf_analysis.definitions import *\n", + "import pandas as pd\n", + "from os import path\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "plt.style.use('bmh')" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Intensity Duration Frequency Analyse - EXTENDED" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "# sub-folder for the results\n", + "output_directory = path.join('ehyd_112086_idf_data')\n", + "\n", + "# initialize of the analysis class\n", + "idf = IntensityDurationFrequencyAnalyse(series_kind=SERIES.PARTIAL, worksheet=METHOD.KOSTRA, extended_durations=True)\n", + "\n", + "# reading the pandas series of the precipitation (data from ehyd.gv.at - ID=112086)\n", + "series = pd.read_parquet('ehyd_112086.parquet')['precipitation']\n", + "\n", + "# setting the series for the analysis\n", + "idf.set_series(series)\n", + "\n", + "# auto-save the calculated parameter so save time for a later use\n", + "idf.auto_save_parameters(path.join(output_directory, 'idf_parameters.yaml'))" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [ + { + "data": { + "text/plain": "('Columns: ',\n Index(['start', 'end', 'duration', 'rain_sum', 'last_event'], dtype='object'),\n '| Number of events: ',\n 1356)" + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "events = idf.rain_events\n", + "\"Columns: \", events.columns, \"| Number of events: \", events.index.size" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 13, + "outputs": [ + { + "data": { + "text/plain": "('Columns: ',\n Index(['start', 'end', 'duration', 'rain_sum', 'last_event'], dtype='object'),\n '| Number of events: ',\n 252)" + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# reduce number of event by limiting the minimum sum of rainfall\n", + "events = events[events[COL.LP] > 10].copy()\n", + "\"Columns: \", events.columns, \"| Number of events: \", events.index.size" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 14, + "outputs": [ + { + "data": { + "text/plain": "('Columns: ',\n Index(['start', 'end', 'duration', 'rain_sum', 'last_event',\n 'max_return_period', 'max_return_period_duration'],\n dtype='object'),\n '| Number of events: ',\n 252)" + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# add the maximum return period to the events and at which duration this period occurs\n", + "idf.add_max_return_periods_to_events(events)\n", + "\"Columns: \", events.columns, \"| Number of events: \", events.index.size" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 15, + "outputs": [ + { + "data": { + "text/plain": "('Columns: ',\n Index(['start', 'end', 'duration', 'rain_sum', 'last_event',\n 'max_return_period', 'max_return_period_duration'],\n dtype='object'),\n '| Number of events: ',\n 19)" + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# reduce number of event by limiting the minimum return period of an event\n", + "events = events[events[COL.MAX_PERIOD] > 2].copy()\n", + "\"Columns: \", events.columns, \"| Number of events: \", events.index.size" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 16, + "outputs": [ + { + "data": { + "text/plain": " start end duration rain_sum \\\n105 2008-06-23 19:50:00 2008-06-23 23:23:00 0 days 03:33:00 34.9 \n125 2008-07-17 14:14:00 2008-07-18 06:27:00 0 days 16:13:00 74.6 \n127 2008-07-20 17:43:00 2008-07-21 03:55:00 0 days 10:12:00 27.7 \n277 2009-07-18 08:55:00 2009-07-18 13:19:00 0 days 04:24:00 58.0 \n286 2009-08-21 19:49:00 2009-08-21 20:29:00 0 days 00:40:00 34.2 \n289 2009-08-28 23:41:00 2009-08-29 00:42:00 0 days 01:01:00 49.3 \n291 2009-09-04 00:31:00 2009-09-04 18:18:00 0 days 17:47:00 69.0 \n566 2011-08-03 19:52:00 2011-08-04 07:45:00 0 days 11:53:00 54.7 \n684 2012-07-14 16:54:00 2012-07-15 13:07:00 0 days 20:13:00 59.8 \n797 2013-05-05 20:46:00 2013-05-07 12:41:00 1 days 15:55:00 119.6 \n844 2013-08-27 17:09:00 2013-08-27 23:48:00 0 days 06:39:00 43.3 \n845 2013-08-28 11:41:00 2013-08-28 16:22:00 0 days 04:41:00 28.5 \n947 2014-05-11 10:20:00 2014-05-11 23:18:00 0 days 12:58:00 55.7 \n948 2014-05-12 17:22:00 2014-05-13 17:27:00 1 days 00:05:00 20.6 \n1137 2015-07-08 11:50:00 2015-07-09 00:26:00 0 days 12:36:00 77.8 \n1261 2016-06-05 11:03:00 2016-06-05 14:57:00 0 days 03:54:00 40.4 \n1294 2016-07-21 22:28:00 2016-07-21 23:21:00 0 days 00:53:00 37.2 \n1298 2016-07-25 10:18:00 2016-07-25 15:48:00 0 days 05:30:00 33.8 \n1300 2016-07-27 15:25:00 2016-07-27 20:25:00 0 days 05:00:00 12.2 \n\n last_event max_return_period max_return_period_duration \n105 2 days 21:52:00 5.363788 15.0 \n125 0 days 04:09:00 6.457449 120.0 \n127 2 days 02:48:00 2.831013 5760.0 \n277 2 days 01:56:00 3.422038 240.0 \n286 4 days 03:04:00 3.930161 20.0 \n289 1 days 03:44:00 20.299093 20.0 \n291 5 days 11:06:00 2.605659 1080.0 \n566 1 days 23:09:00 12.962866 20.0 \n684 1 days 01:15:00 3.720531 8640.0 \n797 0 days 17:29:00 30.584981 2880.0 \n844 0 days 13:56:00 2.452099 5.0 \n845 0 days 11:53:00 3.543837 2880.0 \n947 0 days 13:15:00 2.082195 2880.0 \n948 0 days 18:04:00 2.798822 4320.0 \n1137 8 days 13:52:00 6.054031 720.0 \n1261 0 days 19:55:00 6.537112 5.0 \n1294 5 days 00:03:00 2.426623 30.0 \n1298 0 days 19:00:00 3.400347 5760.0 \n1300 1 days 01:04:00 4.663787 8640.0 ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
startenddurationrain_sumlast_eventmax_return_periodmax_return_period_duration
1052008-06-23 19:50:002008-06-23 23:23:000 days 03:33:0034.92 days 21:52:005.36378815.0
1252008-07-17 14:14:002008-07-18 06:27:000 days 16:13:0074.60 days 04:09:006.457449120.0
1272008-07-20 17:43:002008-07-21 03:55:000 days 10:12:0027.72 days 02:48:002.8310135760.0
2772009-07-18 08:55:002009-07-18 13:19:000 days 04:24:0058.02 days 01:56:003.422038240.0
2862009-08-21 19:49:002009-08-21 20:29:000 days 00:40:0034.24 days 03:04:003.93016120.0
2892009-08-28 23:41:002009-08-29 00:42:000 days 01:01:0049.31 days 03:44:0020.29909320.0
2912009-09-04 00:31:002009-09-04 18:18:000 days 17:47:0069.05 days 11:06:002.6056591080.0
5662011-08-03 19:52:002011-08-04 07:45:000 days 11:53:0054.71 days 23:09:0012.96286620.0
6842012-07-14 16:54:002012-07-15 13:07:000 days 20:13:0059.81 days 01:15:003.7205318640.0
7972013-05-05 20:46:002013-05-07 12:41:001 days 15:55:00119.60 days 17:29:0030.5849812880.0
8442013-08-27 17:09:002013-08-27 23:48:000 days 06:39:0043.30 days 13:56:002.4520995.0
8452013-08-28 11:41:002013-08-28 16:22:000 days 04:41:0028.50 days 11:53:003.5438372880.0
9472014-05-11 10:20:002014-05-11 23:18:000 days 12:58:0055.70 days 13:15:002.0821952880.0
9482014-05-12 17:22:002014-05-13 17:27:001 days 00:05:0020.60 days 18:04:002.7988224320.0
11372015-07-08 11:50:002015-07-09 00:26:000 days 12:36:0077.88 days 13:52:006.054031720.0
12612016-06-05 11:03:002016-06-05 14:57:000 days 03:54:0040.40 days 19:55:006.5371125.0
12942016-07-21 22:28:002016-07-21 23:21:000 days 00:53:0037.25 days 00:03:002.42662330.0
12982016-07-25 10:18:002016-07-25 15:48:000 days 05:30:0033.80 days 19:00:003.4003475760.0
13002016-07-27 15:25:002016-07-27 20:25:000 days 05:00:0012.21 days 01:04:004.6637878640.0
\n
" + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "events" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 18, + "outputs": [ + { + "data": { + "text/plain": "start 2008-07-17 14:14:00\nend 2008-07-18 06:27:00\nduration 0 days 16:13:00\nrain_sum 74.6\nlast_event 0 days 04:09:00\nmax_return_period 6.457449\nmax_return_period_duration 120.0\nName: 125, dtype: object" + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# lets pick one event\n", + "event = events.loc[125]\n", + "event\n", + "\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 19, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n" + ] + }, + { + "data": { + "text/plain": "
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\n" 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# you can also reduce the displayed duration steps\n", + "fig, caption = idf.event_plot(event, durations=idf.duration_steps[:11])\n", + "fig.tight_layout()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt new file mode 100644 index 0000000..1607d1f --- /dev/null +++ b/_sources/index.rst.txt @@ -0,0 +1,25 @@ +.. Intensity Duration Frequency Analyse documentation master file, created by + sphinx-quickstart on Mon Feb 4 18:14:33 2019. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to Intensity Duration Frequency Analyse's documentation! +================================================================ + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + README + api + base_functions + examples/example_commandline + examples/example_python_api + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/_static/basic.css b/_static/basic.css new file mode 100644 index 0000000..30fee9d --- /dev/null +++ b/_static/basic.css @@ -0,0 +1,925 @@ +/* + * basic.css + * ~~~~~~~~~ + * + * Sphinx stylesheet -- basic theme. + * + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +/* -- main layout ----------------------------------------------------------- */ + +div.clearer { + clear: both; +} + +div.section::after { + display: block; + content: ''; + clear: left; +} + +/* -- relbar ---------------------------------------------------------------- */ + +div.related { + width: 100%; + font-size: 90%; +} + +div.related h3 { + display: none; +} + +div.related ul { + margin: 0; + padding: 0 0 0 10px; + list-style: none; +} + +div.related li { + display: inline; +} + +div.related li.right { + float: right; + margin-right: 5px; +} + +/* -- sidebar --------------------------------------------------------------- */ + +div.sphinxsidebarwrapper { + padding: 10px 5px 0 10px; +} + +div.sphinxsidebar { + float: left; + width: 230px; + margin-left: -100%; + font-size: 90%; + word-wrap: break-word; + overflow-wrap : break-word; +} + +div.sphinxsidebar ul { + list-style: none; +} + +div.sphinxsidebar ul ul, +div.sphinxsidebar ul.want-points { + margin-left: 20px; + list-style: square; +} + +div.sphinxsidebar ul ul { + margin-top: 0; + margin-bottom: 0; +} + +div.sphinxsidebar form { + margin-top: 10px; +} + +div.sphinxsidebar input { + border: 1px solid #98dbcc; + font-family: sans-serif; + font-size: 1em; +} + +div.sphinxsidebar #searchbox form.search { + overflow: hidden; +} + +div.sphinxsidebar #searchbox input[type="text"] { + float: left; + width: 80%; + padding: 0.25em; + box-sizing: border-box; +} + +div.sphinxsidebar #searchbox input[type="submit"] { + float: left; + width: 20%; + border-left: none; + padding: 0.25em; + box-sizing: border-box; +} + + +img { + border: 0; + max-width: 100%; +} + +/* -- search page ----------------------------------------------------------- */ + +ul.search { + margin: 10px 0 0 20px; + padding: 0; +} + +ul.search li { + padding: 5px 0 5px 20px; + background-image: url(file.png); + background-repeat: no-repeat; + background-position: 0 7px; +} + +ul.search li a { + font-weight: bold; +} + +ul.search li p.context { + color: #888; + margin: 2px 0 0 30px; + text-align: left; +} + +ul.keywordmatches li.goodmatch a { + font-weight: bold; +} + +/* -- index page ------------------------------------------------------------ */ + +table.contentstable { + width: 90%; + margin-left: auto; + margin-right: auto; +} + +table.contentstable p.biglink { + line-height: 150%; +} + +a.biglink { + font-size: 1.3em; +} + +span.linkdescr { + font-style: italic; + padding-top: 5px; + font-size: 90%; +} + +/* -- general index --------------------------------------------------------- */ + +table.indextable { + width: 100%; +} + +table.indextable td { + text-align: left; + vertical-align: top; +} + +table.indextable ul { + margin-top: 0; + margin-bottom: 0; + list-style-type: none; +} + +table.indextable > tbody > tr > td > ul { + padding-left: 0em; +} + +table.indextable tr.pcap { + height: 10px; +} + +table.indextable tr.cap { + margin-top: 10px; + background-color: #f2f2f2; +} + +img.toggler { + margin-right: 3px; + margin-top: 3px; + cursor: pointer; +} + +div.modindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +div.genindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +/* -- domain module index --------------------------------------------------- */ + +table.modindextable td { + padding: 2px; + border-collapse: collapse; +} + +/* -- general body styles --------------------------------------------------- */ + +div.body { + min-width: 360px; + max-width: 800px; +} + +div.body p, div.body dd, div.body li, div.body blockquote { + -moz-hyphens: auto; + -ms-hyphens: auto; + -webkit-hyphens: auto; + hyphens: auto; +} + +a.headerlink { + visibility: hidden; +} + +a:visited { + color: #551A8B; +} + +h1:hover > a.headerlink, +h2:hover > a.headerlink, +h3:hover > a.headerlink, +h4:hover > a.headerlink, +h5:hover > a.headerlink, +h6:hover > a.headerlink, +dt:hover > a.headerlink, +caption:hover > a.headerlink, +p.caption:hover > a.headerlink, +div.code-block-caption:hover > a.headerlink { + visibility: visible; +} + +div.body p.caption { + text-align: inherit; +} + +div.body td { + text-align: left; +} + +.first { + margin-top: 0 !important; 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+ } +} \ No newline at end of file diff --git a/_static/doctools.js b/_static/doctools.js new file mode 100644 index 0000000..d06a71d --- /dev/null +++ b/_static/doctools.js @@ -0,0 +1,156 @@ +/* + * doctools.js + * ~~~~~~~~~~~ + * + * Base JavaScript utilities for all Sphinx HTML documentation. + * + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ +"use strict"; + +const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", +]); + +const _ready = (callback) => { + if (document.readyState !== "loading") { + callback(); + } else { + document.addEventListener("DOMContentLoaded", callback); + } +}; + +/** + * Small JavaScript module for the documentation. + */ +const Documentation = { + init: () => { + Documentation.initDomainIndexTable(); + Documentation.initOnKeyListeners(); + }, + + /** + * i18n support + */ + TRANSLATIONS: {}, + PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), + LOCALE: "unknown", + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext: (string) => { + const translated = Documentation.TRANSLATIONS[string]; + switch (typeof translated) { + case "undefined": + return string; // no translation + case "string": + return translated; // translation exists + default: + return translated[0]; // (singular, plural) translation tuple exists + } + }, + + ngettext: (singular, plural, n) => { + const translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated !== "undefined") + return translated[Documentation.PLURAL_EXPR(n)]; + return n === 1 ? singular : plural; + }, + + addTranslations: (catalog) => { + Object.assign(Documentation.TRANSLATIONS, catalog.messages); + Documentation.PLURAL_EXPR = new Function( + "n", + `return (${catalog.plural_expr})` + ); + Documentation.LOCALE = catalog.locale; + }, + + /** + * helper function to focus on search bar + */ + focusSearchBar: () => { + document.querySelectorAll("input[name=q]")[0]?.focus(); + }, + + /** + * Initialise the domain index toggle buttons + */ + initDomainIndexTable: () => { + const toggler = (el) => { + const idNumber = el.id.substr(7); + const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); + if (el.src.substr(-9) === "minus.png") { + el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; + toggledRows.forEach((el) => (el.style.display = "none")); + } else { + el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; + toggledRows.forEach((el) => (el.style.display = "")); + } + }; + + const togglerElements = document.querySelectorAll("img.toggler"); + togglerElements.forEach((el) => + el.addEventListener("click", (event) => toggler(event.currentTarget)) + ); + togglerElements.forEach((el) => (el.style.display = "")); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); + }, + + initOnKeyListeners: () => { + // only install a listener if it is really needed + if ( + !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && + !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS + ) + return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.altKey || event.ctrlKey || event.metaKey) return; + + if (!event.shiftKey) { + switch (event.key) { + case "ArrowLeft": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const prevLink = document.querySelector('link[rel="prev"]'); + if (prevLink && prevLink.href) { + window.location.href = prevLink.href; + event.preventDefault(); + } + break; + case "ArrowRight": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const nextLink = document.querySelector('link[rel="next"]'); + if (nextLink && nextLink.href) { + window.location.href = nextLink.href; + event.preventDefault(); + } + break; + } + } + + // some keyboard layouts may need Shift to get / + switch (event.key) { + case "/": + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; + Documentation.focusSearchBar(); + event.preventDefault(); + } + }); + }, +}; + +// quick alias for translations +const _ = Documentation.gettext; + +_ready(Documentation.init); diff --git a/_static/documentation_options.js b/_static/documentation_options.js new file mode 100644 index 0000000..4029149 --- /dev/null +++ b/_static/documentation_options.js @@ -0,0 +1,13 @@ +const DOCUMENTATION_OPTIONS = { + VERSION: '0.2.3', + LANGUAGE: 'en', + COLLAPSE_INDEX: false, + BUILDER: 'html', + FILE_SUFFIX: '.html', + LINK_SUFFIX: '.html', + HAS_SOURCE: true, + SOURCELINK_SUFFIX: '.txt', + NAVIGATION_WITH_KEYS: false, + SHOW_SEARCH_SUMMARY: true, + ENABLE_SEARCH_SHORTCUTS: true, +}; 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+ + +/* Non-minified version is copied as a separate JS file, is available */ + +/** + * Porter Stemmer + */ +var Stemmer = function() { + + var step2list = { + ational: 'ate', + tional: 'tion', + enci: 'ence', + anci: 'ance', + izer: 'ize', + bli: 'ble', + alli: 'al', + entli: 'ent', + eli: 'e', + ousli: 'ous', + ization: 'ize', + ation: 'ate', + ator: 'ate', + alism: 'al', + iveness: 'ive', + fulness: 'ful', + ousness: 'ous', + aliti: 'al', + iviti: 'ive', + biliti: 'ble', + logi: 'log' + }; + + var step3list = { + icate: 'ic', + ative: '', + alize: 'al', + iciti: 'ic', + ical: 'ic', + ful: '', + ness: '' + }; + + var c = "[^aeiou]"; // consonant + var v = "[aeiouy]"; // vowel + var C = c + "[^aeiouy]*"; // consonant sequence + var V = v + "[aeiou]*"; // vowel sequence + + var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0 + var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1 + var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 + var s_v = "^(" + C + ")?" + v; // vowel in stem + + this.stemWord = function (w) { + var stem; + var suffix; + var firstch; + var origword = w; + + if (w.length < 3) + return w; + + var re; + var re2; + var re3; + var re4; + + firstch = w.substr(0,1); + if (firstch == "y") + w = firstch.toUpperCase() + w.substr(1); + + // Step 1a + re = /^(.+?)(ss|i)es$/; + re2 = /^(.+?)([^s])s$/; + + if (re.test(w)) + w = w.replace(re,"$1$2"); + else if (re2.test(w)) + w = w.replace(re2,"$1$2"); + + // Step 1b + re = /^(.+?)eed$/; + re2 = /^(.+?)(ed|ing)$/; + if (re.test(w)) { + var fp = re.exec(w); + re = new RegExp(mgr0); + if (re.test(fp[1])) { + re = /.$/; + w = w.replace(re,""); + } + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = new RegExp(s_v); + if (re2.test(stem)) { + w = stem; + re2 = /(at|bl|iz)$/; + re3 = new RegExp("([^aeiouylsz])\\1$"); + re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re2.test(w)) + w = w + "e"; + else if (re3.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + else if (re4.test(w)) + w = w + "e"; + } + } + + // Step 1c + re = /^(.+?)y$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(s_v); + if (re.test(stem)) + w = stem + "i"; + } + + // Step 2 + re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step2list[suffix]; + } + + // Step 3 + re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step3list[suffix]; + } + + // Step 4 + re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; + re2 = /^(.+?)(s|t)(ion)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + if (re.test(stem)) + w = stem; + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1] + fp[2]; + re2 = new RegExp(mgr1); + if (re2.test(stem)) + w = stem; + } + + // Step 5 + re = /^(.+?)e$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + re2 = new RegExp(meq1); + re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) + w = stem; + } + re = /ll$/; + re2 = new RegExp(mgr1); + if (re.test(w) && re2.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + + // and turn initial Y back to y + if (firstch == "y") + w = firstch.toLowerCase() + w.substr(1); + return w; + } +} + diff --git a/_static/minus.png b/_static/minus.png new file mode 100644 index 0000000000000000000000000000000000000000..d96755fdaf8bb2214971e0db9c1fd3077d7c419d GIT binary patch literal 90 zcmeAS@N?(olHy`uVBq!ia0vp^+#t*WBp7;*Yy1LIik>cxAr*|t7R?Mi>2?kWtu=nj kDsEF_5m^0CR;1wuP-*O&G^0G}KYk!hp00i_>zopr08q^qX#fBK literal 0 HcmV?d00001 diff --git a/_static/nature.css b/_static/nature.css new file mode 100644 index 0000000..84c584a --- /dev/null +++ b/_static/nature.css @@ -0,0 +1,252 @@ +/* + * nature.css_t + * ~~~~~~~~~~~~ + * + * Sphinx stylesheet -- nature theme. + * + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +@import url("basic.css"); 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font-weight: bold } /* Literal.Number.Integer.Long */ \ No newline at end of file diff --git a/_static/searchtools.js b/_static/searchtools.js new file mode 100644 index 0000000..7918c3f --- /dev/null +++ b/_static/searchtools.js @@ -0,0 +1,574 @@ +/* + * searchtools.js + * ~~~~~~~~~~~~~~~~ + * + * Sphinx JavaScript utilities for the full-text search. + * + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ +"use strict"; + +/** + * Simple result scoring code. + */ +if (typeof Scorer === "undefined") { + var Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms, highlightTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + const contentRoot = document.documentElement.dataset.content_root; + + const [docName, title, anchor, descr, score, _filename] = item; + + let listItem = document.createElement("li"); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = contentRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = contentRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) { + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + // highlight search terms in the description + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + } + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms) + ); + // highlight search terms in the summary + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = _( + `Search finished, found ${resultCount} page(s) matching the search query.` + ); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms, + highlightTerms, +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms, highlightTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() }); + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent !== undefined) return docContent.textContent; + console.warn( + "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + /** + * execute search (requires search index to be loaded) + */ + query: (query) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + // array of [docname, title, anchor, descr, score, filename] + let results = []; + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + let score = Math.round(100 * queryLower.length / title.length) + results.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id] of foundEntries) { + let score = Math.round(100 * queryLower.length / entry.length) + results.push([ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // lookup as object + objectTerms.forEach((term) => + results.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + results.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item))); + + // now sort the results by score (in opposite order of appearance, since the + // display function below uses pop() to retrieve items) and then + // alphabetically + results.sort((a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; + }); + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + results = results.reverse(); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms, highlightTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord) && !terms[word]) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord) && !titleTerms[word]) + arr.push({ files: titleTerms[word], score: Scorer.partialTitle }); + }); + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1) + fileMap.get(file).push(word); + else fileMap.set(file, [word]); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords) => { + const text = Search.htmlToText(htmlText); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); diff --git a/_static/sphinx_highlight.js b/_static/sphinx_highlight.js new file mode 100644 index 0000000..8a96c69 --- /dev/null +++ b/_static/sphinx_highlight.js @@ -0,0 +1,154 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + const rest = document.createTextNode(val.substr(pos + text.length)); + parent.insertBefore( + span, + parent.insertBefore( + rest, + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + /* There may be more occurrences of search term in this node. 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' + + '' + + _("Hide Search Matches") + + "

" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(() => { + /* Do not call highlightSearchWords() when we are on the search page. + * It will highlight words from the *previous* search query. + */ + if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); + SphinxHighlight.initEscapeListener(); +}); diff --git a/api.html b/api.html new file mode 100644 index 0000000..439d765 --- /dev/null +++ b/api.html @@ -0,0 +1,665 @@ + + + + + + + + API — Intensity Duration Frequency Analyse 0.2.3 documentation + + + + + + + + + + + + + +
+
+
+
+ +
+

API

+
+

Main Function

+
+
+class idf_analysis.idf_class.IntensityDurationFrequencyAnalyse(series_kind='partial', worksheet='KOSTRA', extended_durations=False)[source]
+

heavy rain as a function of the duration and the return period acc. to DWA-A 531 (2012)

+

This program reads the measurement data of the rainfall +and calculates the distribution of the rainfall as a function of the return period and the duration

+

for duration steps up to 12 hours (and more) and return period in a range of ‘0.5a <= T_n <= 100a’

+
+
+_series
+

rain time-series

+
+
Type:
+

pandas.Series

+
+
+
+ +
+
+_freq
+

frequency of the rain series

+
+
Type:
+

pandas.DateOffset

+
+
+
+ +
+
+_return_periods_frame
+

with return periods of all given durations

+
+
Type:
+

pandas.DataFrame

+
+
+
+ +
+
+_rain_events
+
+
Type:
+

pandas.DataFrame

+
+
+
+ +
+
+_rainfall_sum_frame
+

with rain sums of all given durations

+
+
Type:
+

pandas.DataFrame

+
+
+
+ +
+
+add_max_intensities_to_events(events)[source]
+

Add the maximum intensities for all duration steps to the events table.

+
+
Parameters:
+

events (pandas.DataFrame) – events table

+
+
Returns:
+

events table including the columns with the maximum intensities

+
+
Return type:
+

pandas.DataFrame

+
+
+
+ +
+
+auto_save_parameters(filename)[source]
+

Auto-save the parameters as a yaml-file to save computation time.

+
+ +
+
+auto_save_rain_events(filename, sep=';', decimal='.')[source]
+

auto-save the rain-events dataframe as a csv-file to save computation time.

+
+ +
+
+auto_save_return_periods_frame(filename)[source]
+

auto-save the return-periods dataframe as a parquet-file to save computation time.

+
+ +
+
+depth_of_rainfall(duration, return_period)[source]
+

calculate the height of the rainfall h in L/m² = mm

+
+
Parameters:
+
+
+
Returns:
+

height of the rainfall h in L/m² = mm

+
+
Return type:
+

int | float | list | numpy.ndarray | pandas.Series

+
+
+
+ +
+
+property duration_steps
+

get duration steps (in minutes) for the parameter calculation and basic evaluations

+
+
Returns:
+

duration steps in minutes

+
+
Return type:
+

list | numpy.ndarray

+
+
+
+ +
+
+property duration_steps_for_output
+

get duration steps (in minutes) for the parameter calculation and basic evaluations

+
+
Returns:
+

duration steps in minutes

+
+
Return type:
+

list | numpy.ndarray

+
+
+
+ +
+
+event_report(filename, min_event_rain_sum=25, min_return_period=0.5, durations=None)[source]
+

create pdf file with the biggest rain events +for each event is represented by a plot of the rain series +and a IDF analysis where the return periods are calculated

+
+
Parameters:
+
    +

  • filename (str) – path (directory + filename) for the created pdf-report

    • +

  • min_event_rain_sum (float) – only events with a bigger rain sum will be created

    • +

  • min_return_period (float) – only events with a bigger return period will be analysed +(the plot will be created anyway)

    • +

  • durations (list[int]) – analysed durations +(default: [5, 10, 15, 20, 30, 45, 60, 90, 120, 180, 240, 360, 540, 720, 1080, 1440, 2880, 4320])

    • +
+
+
+
+ +
+
+classmethod from_idf_table(idf_table, worksheet='KOSTRA')[source]
+

Create an IDF-analysis-object based on an idf-tabel (i.e. from a given KOSTRA table)

+
+
Parameters:
+
    +

  • idf_table (pandas.DataFrame) – idf-table with index=Durations and columns=return Period and values=Rainheight

    • +

  • worksheet (str | optional) – name of the worksheet to use. default: ‘KOSTRA’

    • +
+
+
Returns:
+

idf-object

+
+
Return type:
+

IntensityDurationFrequencyAnalyse

+
+
+
+ +
+
+get_duration(height_of_rainfall, return_period)[source]
+

calculate the duration, when the height of rainfall and the return period are given

+
+
Parameters:
+
    +

  • height_of_rainfall (float) – in [mm]

    • +

  • return_period (float) – in years

    • +
+
+
Returns:
+

duration in minutes

+
+
Return type:
+

float

+
+
+
+ +
+
+get_rainfall_sum_frame(series=None, durations=None)[source]
+

Get a rainfall sum frame for any series with the duration steps as columns.

+

Default: The time-series and the duration-steps of the analysis.

+
+
Parameters:
+
    +

  • series (pandas.Series, Optional) – rainfall time-series

    • +

  • durations (list, Optional) – list of durations in minutes which are of interest (default: pre-defined durations)

    • +
+
+
Returns:
+

Rain sum depending on the duration per datetime-index.

+
+
Return type:
+

pandas.DataFrame

+
+
+
+ +
+
+get_return_period(height_of_rainfall, duration)[source]
+

calculate the return period, when the height of rainfall and the duration are given

+
+
Parameters:
+
+
+
Returns:
+

return period in years

+
+
Return type:
+

int | float | list | numpy.ndarray | pandas.Series

+
+
+
+ +
+
+get_return_periods_frame(series=None, durations=None)[source]
+

Get the return periods for any time-series with the duration steps as columns.

+

Default: The time-series and the duration-steps of the analysis.

+
+
Parameters:
+
    +

  • series (pandas.Series, Optional) – rainfall time-series

    • +

  • durations (list, Optional) – Durations in minutes which are of interest (default: pre-defined durations)

    • +
+
+
Returns:
+

Return periods depending on the duration per datetime-index.

+
+
Return type:
+

pandas.DataFrame

+
+
+
+ +
+
+property model_rain_block
+

Create a model block rain class.

+
+
Returns:
+

Synthetic model block rain.

+
+
Return type:
+

_BlockRain

+
+
+
+ +
+
+property model_rain_euler
+

Create a model Euler rain class.

+
+
Returns:
+

Synthetic model Euler rain.

+
+
Return type:
+

_EulerRain

+
+
+
+ +
+
+property parameters
+

get the calculation parameters

+

calculation method depending on the used worksheet and on the duration +also the parameters for each method

+

to save some time and save the parameters with +IntensityDurationFrequencyAnalyse.write_parameters() +and read them later with IntensityDurationFrequencyAnalyse.read_parameters()

+
+
Returns:
+

calculation parameters

+
+
Return type:
+

IdfParameters

+
+
+
+ +
+
+r_720_1()[source]
+

rain flow rate in [l/(s*ha)] for a duration of 12h and a return period of 1 year

+
+
Returns:
+

rain flow rate in [l/(s*ha)]

+
+
Return type:
+

float

+
+
+
+ +
+
+property rain_events
+

get the all the rain events of the time-series

+

default minimal gap between events is 4 hours

+
+
Returns:
+

data-frame of events with start-, end-time and duration

+
+
Return type:
+

pandas.DataFrame

+
+
+
+ +
+
+rain_flow_rate(duration, return_period)[source]
+

convert the height of rainfall to the specific rain flow rate in [l/(s*ha)] +if 2 array-like parameters are give, a element-wise calculation will be made. +So the length of the array must be the same.

+
+
Parameters:
+
+
+
Returns:
+

specific rain flow rate in [l/(s*ha)]

+
+
Return type:
+

int | float | list | numpy.ndarray | pandas.Series

+
+
+
+ +
+
+property rainfall_sum_frame
+

Get the rainfall sum over the whole time-series for the default duration steps.

+
+
Returns:
+

Rain sum depending on the duration per datetime-index.

+
+
Return type:
+

pandas.DataFrame

+
+
+
+ +
+
+read_parameters(filename)[source]
+

Read parameters from a .yaml-file to save computation time.

+

Extract interim results from parameters.

+
+
Parameters:
+

filename (str, Path) – filename of the parameters yaml-file

+
+
+
+ +
+
+read_rain_events(filename, sep=';', decimal='.')[source]
+

read the rain-events dataframe as a csv-file to save computation time.

+
+ +
+
+read_return_periods_frame(filename, **kwargs)[source]
+

read the return-periods dataframe as a parquet-file to save computation time.

+
+ +
+
+result_figure(min_duration=5.0, max_duration=720.0, logx=False, return_periods=None, color=True, ax=None, linestyle=None, add_interim=False, alpha=1)[source]
+

Create a plot with the idf-curves with duration on the x-axis and rainfall depth on the y-axis.

+
+
Parameters:
+
    +

  • min_duration (float) – Shortest duration on the plot.

    • +

  • max_duration (float) – Longest duration on the plot.

    • +

  • logx (bool) – Use a logarithmic scale on the x-axis.

    • +

  • return_periods (list[int] | Optional) – List of return periods to plot. Default = [1, 2, 5, 10, 50, 100]

    • +

  • color (bool) – Use color and a legend to differentiate between the return periods (default). +Otherwise, annotation text and black lines.

    • +

  • ax (plt.Axes) – Axes to plot on. Default = create new one.

    • +

  • linestyle (str) – Line-style for the plotted lines.

    • +

  • add_interim (bool) – Add interim results from the series analysis as scatter points.

    • +

  • alpha (float) – Alpha of the idf-lines in the plot.

    • +
+
+
Returns:
+

figure and axes of the plot.

+
+
Return type:
+

(plt.Figure, plt.Axes)

+
+
+
+ +
+
+result_table(durations=None, return_periods=None, add_names=False)[source]
+

Get an idf-table of rainfall depth with return periods as columns and durations as rows.

+
+
Parameters:
+
    +

  • durations (list | numpy.ndarray) – list of durations in minutes for the table

    • +

  • return_periods (list) – list of return periods in years for the table

    • +

  • add_names (bool) – weather to use expressive names as index-&column-label

    • +
+
+
Returns:
+

idf table

+
+
Return type:
+

pandas.DataFrame

+
+
+
+ +
+
+property return_periods_frame
+

Get the return periods over the whole time-series for the default duration steps.

+
+
Returns:
+

data-frame of return periods where the columns are the duration steps

+
+
Return type:
+

pandas.DataFrame

+
+
+
+ +
+
+set_series(series)[source]
+

set the series for the analysis

+
+
Parameters:
+

series (pandas.Series) – precipitation time-series

+
+
+
+ +
+
+write_parameters(filename)[source]
+

save parameters as yaml-file to save computation time.

+
+
Parameters:
+

filename (str) – filename for the parameters yaml-file

+
+
+
+ +
+
+write_rain_events(filename, sep=';', decimal='.')[source]
+

save the rain-events dataframe as a csv-file for external use or to save computation time.

+
+ +
+
+write_return_periods_frame(filename, **kwargs)[source]
+

save the return-periods dataframe as a parquet-file to save computation time.

+
+ +
+ +
+
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/base_functions.html b/base_functions.html new file mode 100644 index 0000000..b8864fa --- /dev/null +++ b/base_functions.html @@ -0,0 +1,709 @@ + + + + + + + + Base Functions — Intensity Duration Frequency Analyse 0.2.3 documentation + + + + + + + + + + + + + +
+
+
+
+ +
+

Base Functions

+
+

Calculation Methods

+

Functions to analyse the precipitations series based on the core method.

+
+
+idf_analysis.event_series_analysis.annual_series(rolling_sum_values, year_index)[source]
+

Create an annual series of the maximum overlapping sum per year and calculate the “u” and “w” parameters.

+

acc. to DWA-A 531 chap. 5.1.5

+

Gumbel distribution | https://en.wikipedia.org/wiki/Gumbel_distribution

+
+
Parameters:
+
    +

  • rolling_sum_values (numpy.ndarray) – Array with maximum rolling sum per event per year.

    • +

  • year_index (numpy.ndarray) – Array with year of the event.

    • +
+
+
Returns:
+

Parameter u and w from the annual series for a specific duration step as a tuple.

+
+
Return type:
+

dict[str, float]

+
+
+
+ +
+
+idf_analysis.event_series_analysis.calculate_u_w(file_input, duration_steps, series_kind)[source]
+

Statistical analysis for each duration step.

+

acc. to DWA-A 531 chap. 5.1

+

Save the parameters of the distribution function as interim results.

+

acc. to DWA-A 531 chap. 4.4: use the annual series only for measurement periods over 20 years

+
+
Parameters:
+
+
+
Returns:
+

with key=durations and values=dict(u, w)

+
+
Return type:
+

dict[int, dict]

+
+
+
+ +
+
+idf_analysis.event_series_analysis.iter_event_series(file_input, duration_steps)[source]
+

Statistical analysis for each duration step.

+

acc. to DWA-A 531 chap. 5.1

+

Save the parameters of the distribution function as interim results.

+
+
Parameters:
+
+
+
Yields:
+

series – max rainfall intensity per event for each duration step

+
+
+
+ +
+
+idf_analysis.event_series_analysis.partial_series(rolling_sum_values, measurement_period)[source]
+

Create a partial series of the largest overlapping sums and calculate the “u” and “w” parameters.

+

acc. to DWA-A 531 chap. 5.1.4

+

Exponential distribution | https://en.wikipedia.org/wiki/Exponential_distribution

+
+
Parameters:
+
    +

  • rolling_sum_values (numpy.ndarray) – Array with maximum rolling sum per event.

    • +

  • measurement_period (float) – Measurement period in years.

    • +
+
+
Returns:
+

parameter u and w from the partial series for a specific duration step as a tuple

+
+
Return type:
+

dict[str, float]

+
+
+
+ +
+
+idf_analysis.parameter_formulations.folded_log_formulation(duration, param, case, param_mean=None, duration_mean=None)[source]
+
+
Parameters:
+
    +

  • duration –

    • +

  • param –

    • +

  • case –

    • +

  • param_mean –

    • +

  • duration_mean –

    • +
+
+
+

Returns:

+
+ +
+
+idf_analysis.parameter_formulations.hyperbolic_formulation(duration: array, param: array, a_start=20.0, b_start=15.0, param_mean=None, duration_mean=None)[source]
+

Computes the hyperbolic formulation using the given parameters.

+
+
Parameters:
+
    +

  • duration (np.array) – Array of durations.

    • +

  • param (np.array) – Array of parameters.

    • +

  • a_start (float) – Initial value for ‘a’ parameter.

    • +

  • b_start (float) – Initial value for ‘b’ parameter.

    • +

  • param_mean (float, optional) – Mean value of parameters. Defaults to None.

    • +

  • duration_mean (float, optional) – Mean value of durations. Defaults to None.

    • +
+
+
Returns:
+

Computed value of ‘a’ and ‘b’.

+
+
Return type:
+

tuple[float, float]

+
+
+
+ +
+
+class idf_analysis.idf_backend.IdfParameters(series_kind='partial', worksheet='KOSTRA', extended_durations=False)[source]
+
+
+classmethod from_interim_results_file(interim_results_fn, worksheet='KOSTRA')[source]
+

DEPRECIATED: for compatibility reasons. To use the old file and convert it to the new parameters file

+
+ +
+
+get_array_param(p, duration)[source]
+
+
Parameters:
+
    +

  • p (str) – name of the parameter ‘u’ or ‘w’

    • +

  • duration (numpy.ndarray) – in minutes

    • +
+
+
Returns:
+

u, w

+
+
Return type:
+

(numpy.ndarray, numpy.ndarray)

+
+
+
+ +
+
+get_scalar_param(p, duration)[source]
+
+
Parameters:
+
    +

  • p (str) – name of the parameter ‘u’ or ‘w’

    • +

  • duration (float | int) – in minutes

    • +
+
+
Returns:
+

u, w

+
+
Return type:
+

(float, float)

+
+
+
+ +
+
+get_u_w(duration)[source]
+

calculate the u and w parameters depending on the durations

+
+
Parameters:
+

duration (numpy.ndarray| list | float | int) – in minutes

+
+
Returns:
+

u and w

+
+
Return type:
+

(float, float)

+
+
+
+ +
+
+measured_points(return_periods, max_duration=None)[source]
+

get the calculation results of the rainfall with u and w without the estimation of the formulation

+
+
Parameters:
+
    +

  • return_periods (float | np.array | list | pd.Series) – return period in [a]

    • +

  • max_duration (float) – max duration in [min]

    • +
+
+
Returns:
+

series with duration as index and the height of the rainfall as data

+
+
Return type:
+

pd.Series

+
+
+
+ +
+
+set_parameter_approaches_from_worksheet(worksheet)[source]
+

Set approaches depending on the duration and the parameter.

+
+
Parameters:
+

worksheet (str) – worksheet name for the analysis: +- ‘DWA-A_531’ +- ‘ATV-A_121’ +- ‘DWA-A_531_advektiv’ (yet not implemented)

+
+
Returns:
+

table of approaches depending on the duration and the parameter

+
+
Return type:
+

list[dict]

+
+
+
+ +
+ +
+
+

Input and Output

+

Function for reading and writing files.

+
+
+idf_analysis.in_out.import_series(filename, series_label='precipitation', index_label='datetime', csv_reader_args=None)[source]
+
+
Parameters:
+
    +

  • filename –

    • +

  • series_label –

    • +

  • index_label –

    • +

  • csv_reader_args – for example: sep=”,” or “.” and decimal=”;” or “,”

    • +
+
+
Returns:
+

precipitation series

+
+
Return type:
+

pandas.Series

+
+
+
+ +
+
+

SWW Utils

+

Functions to help analyse data in a general way. +Most of this function have been developed on the +institute of urban water management at the university of technology Graz.

+
+
+exception idf_analysis.sww_utils.IdfError[source]
+

Some Error Within this Package

+
+ +
+
+idf_analysis.sww_utils.agg_events(events, series, agg='sum')[source]
+
+
Parameters:
+
+
+
Returns:
+

result of function of every event

+
+
Return type:
+

numpy.ndarray

+
+
+
+ +
+
+idf_analysis.sww_utils.event_duration(events)[source]
+

calculate the event duration

+
+
Parameters:
+

events (pandas.DataFrame) – table of events with COL.START and COL.END times

+
+
Returns:
+

duration of each event

+
+
Return type:
+

pandas.Series

+
+
+
+ +
+
+idf_analysis.sww_utils.event_number_to_series(events, index)[source]
+

make a time-series where the value of the event number is paste to the <index>

+
+
Parameters:
+
+
+
Return type:
+

pandas.Series

+
+
+
+ +
+
+idf_analysis.sww_utils.guess_freq(date_time_index, default=Timedelta('0 days 00:01:00'))[source]
+

guess the frequency by evaluating the most often frequency

+
+
Parameters:
+
+
+
Returns:
+

frequency of the date-time-index

+
+
Return type:
+

pandas.DateOffset

+
+
+
+ +
+
+idf_analysis.sww_utils.rain_bar_plot(rain, ax=None, color='#1E88E5', reverse=False, step='post', joinstyle='miter', capstyle='butt')[source]
+

Make a standard precipitation/rain plot.

+
+
Parameters:
+
    +

  • rain (pandas.Series) –

    • +

  • ax (matplotlib.axes.Axes) –

    • +

  • color (str) –

    • +

  • reverse (bool) –

    • +

  • step (str) – ‘mid’ ‘post’ pre’

    • +
+
+
Returns:
+

rain plot

+
+
Return type:
+

matplotlib.axes.Axes

+
+
+
+ +
+
+idf_analysis.sww_utils.rain_events(series, ignore_rain_below=0.01, min_gap=Timedelta('0 days 04:00:00'))[source]
+

get rain events as a table with start and end times

+
+
Parameters:
+
    +

  • series (pandas.Series) – rain series

    • +

  • ignore_rain_below (float) – where it is considered as rain

    • +

  • min_gap (pandas.Timedelta) – 4 hours of no rain between events

    • +
+
+
Returns:
+

table of the rain events

+
+
Return type:
+

pandas.DataFrame

+
+
+
+ +
+
+idf_analysis.sww_utils.resample_rain_series(series)[source]
+
+
Parameters:
+

series (pandas.Series) –

+
+
Returns:
+

the resampled series AND the final frequency in minutes

+
+
Return type:
+

tuple[pandas.Series, int]

+
+
+
+ +
+
+

Converter helper functions

+

Functions to help convert things and units.

+
+
+idf_analysis.little_helpers.delta2min(time_delta)[source]
+

convert timedelta to float in minutes

+
+
Parameters:
+

time_delta (pandas.Timedelta, pandas.DateOffset) –

+
+
Returns:
+

the timedelta in minutes

+
+
Return type:
+

float

+
+
+
+ +
+
+idf_analysis.little_helpers.duration_steps_readable(durations)[source]
+

convert the durations to a more readable form

+
+
Parameters:
+

durations (list[int | float]) – in minutes

+
+
Returns:
+

of the readable duration list

+
+
Return type:
+

list[str]

+
+
+
+ +
+
+idf_analysis.little_helpers.height2rate(height_of_rainfall, duration)[source]
+

calculate the specific rain flow rate in [l/(s*ha)] +if 2 array-like parameters are give, a element-wise calculation will be made. +So the length of the array must be the same.

+
+
Parameters:
+
    +

  • height_of_rainfall (float | np.ndarray | pd.Series) – height_of_rainfall: in [mm]

    • +

  • duration (float | np.ndarray | pd.Series) – in minutes

    • +
+
+
Returns:
+

specific rain flow rate in [l/(s*ha)]

+
+
Return type:
+

float | np.ndarray | pd.Series

+
+
+
+ +
+
+idf_analysis.little_helpers.minutes_readable(minutes)[source]
+

convert the duration in minutes to a more readable form

+
+
Parameters:
+

minutes (float | int) – duration in minutes

+
+
Returns:
+

duration as a string

+
+
Return type:
+

str

+
+
+
+ +
+
+idf_analysis.little_helpers.rate2height(rain_flow_rate, duration)[source]
+

convert the rain flow rate to the height of rainfall in [mm] +if 2 array-like parameters are give, a element-wise calculation will be made. +So the length of the array must be the same.

+
+
Parameters:
+
    +

  • rain_flow_rate (float | np.ndarray | pd.Series) – in [l/(s*ha)]

    • +

  • duration (float | np.ndarray | pd.Series) – in minutes

    • +
+
+
Returns:
+

height of rainfall in [mm]

+
+
Return type:
+

float | np.ndarray | pd.Series

+
+
+
+ +
+
+idf_analysis.little_helpers.timedelta_components_plus(td, min_freq='T')[source]
+

Schaltjahre nicht miteinbezogen

+
+ +
+
+idf_analysis.little_helpers.timedelta_readable(td, min_freq='T', short=False, sep=', ')[source]
+

Schaltjahre nicht miteinbezogen

+
+ +
+
+

Plotting helper functions

+

Functions to add or manipulate plotting figures.

+
+
+idf_analysis.plot_helpers.idf_bar_axes(ax, idf_table, return_period_colors={1: '#00ffff', 2: '#add8e6', 5: '#0000ff', 10: '#ffff00', 20: '#ffa500', 50: '#ff0000', 100: '#ff00ff'})[source]
+

create

+
+
Parameters:
+
    +

  • ax (matplotlib.pyplot.Axes) –

    • +

  • idf_table (pandas.DataFrame) –

    • +

  • return_period_colors (dict) – color of each return period {return period: color}

    • +
+
+
Return type:
+

matplotlib.pyplot.Axes

+
+
+
+ +
+
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/examples/example_commandline.html b/examples/example_commandline.html new file mode 100644 index 0000000..5c77218 --- /dev/null +++ b/examples/example_commandline.html @@ -0,0 +1,318 @@ + + + + + + + + Example Commandline Use — Intensity Duration Frequency Analyse 0.2.3 documentation + + + + + + + + + + + + + + + + +
+
+
+
+ +
+

Example Commandline Use

+
+
[1]:
+
+
+
! python -m idf_analysis -h
+
+
+
+
+
+
+
+
+usage: __main__.py [-h] -i INPUT
+                   [-ws {ATV-A_121,DWA-A_531,DWA-A_531_advektiv}]
+                   [-kind {partial,annual}] [-t {>= 0.5 a and <= 100 a}]
+                   [-d {>= 5 min and <= 8640 min}] [-r {>= 0 L/s*ha}]
+                   [-h_N {>= 0 mm}] [--r_720_1] [--plot] [--export_table]
+
+heavy rain as a function of the duration and the return period acc. to DWA-A
+531 (2012) All files will be saved in the same directory of the input file but
+in a subfolder called like the inputfile + "_idf_data". Inside this folder a
+file called "idf_parameter.yaml"-file will be saved and contains interim-
+calculation-results and will be automatically reloaded on the next call.
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -i INPUT, --input INPUT
+                        input file with the rain time-series (csv or parquet)
+  -ws {ATV-A_121,DWA-A_531,DWA-A_531_advektiv}, --worksheet {ATV-A_121,DWA-A_531,DWA-A_531_advektiv}
+                        From which worksheet the recommendations for
+                        calculating the parameters should be taken.
+  -kind {partial,annual}, --series_kind {partial,annual}
+                        The kind of series used for the calculation.
+                        (Calculation with partial series is more precise and
+                        recommended.)
+  -t {>= 0.5 a and <= 100 a}, --return_period {>= 0.5 a and <= 100 a}
+                        return period in years (If two of the three variables
+                        (rainfall (height or flow-rate), duration, return
+                        period) are given, the third variable is calculated.)
+  -d {>= 5 min and <= 8640 min}, --duration {>= 5 min and <= 8640 min}
+                        duration in minutes (If two of the three variables
+                        (rainfall (height or flow-rate), duration, return
+                        period) are given, the third variable is calculated.)
+  -r {>= 0 L/(s*ha)}, --flow_rate_of_rainfall {>= 0 L/(s*ha)}
+                        rainfall in Liter/(s * ha) (If two of the three
+                        variables (rainfall (height or flow-rate), duration,
+                        return period) are given, the third variable is
+                        calculated.)
+  -h_N {>= 0 mm}, --height_of_rainfall {>= 0 mm}
+                        rainfall in mm or Liter/m^2 (If two of the three
+                        variables (rainfall (height or flow-rate), duration,
+                        return period) are given, the third variable is
+                        calculated.)
+  --r_720_1             design rainfall with a duration of 720 minutes (=12 h)
+                        and a return period of 1 year
+  --plot                get a plot of the idf relationship
+  --export_table        get a table of the most frequent used values
+
+
+

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

+
+
[2]:
+
+
+
! python -m idf_analysis -i ehyd_112086.parquet
+
+
+
+
+
+
+
+
+Using the subfolder "ehyd_112086_idf_data" for the interim- and final-results.
+Found existing interim-results in "ehyd_112086_idf_data\idf_parameters.yaml" and using them for calculations.
+
+
+
+
[4]:
+
+
+
! python -m idf_analysis -i ehyd_112086.parquet --r_720_1
+
+
+
+
+
+
+
+
+Using the subfolder "ehyd_112086_idf_data" for the interim- and final-results.
+Found existing interim-results in "ehyd_112086_idf_data\idf_parameters.yaml" and using them for calculations.
+Resultierende Regenhöhe h_N(T_n=1.0a, D=720.0min) = 49.29 mm
+Resultierende Regenspende r_N(T_n=1.0a, D=720.0min) = 11.41 L/(s*ha)
+
+
+
+
[7]:
+
+
+
! python -m idf_analysis -i ehyd_112086.parquet -d 720 -t 1
+
+
+
+
+
+
+
+
+Using the subfolder "ehyd_112086_idf_data" for the interim- and final-results.
+Found existing interim-results in "ehyd_112086_idf_data\idf_parameters.yaml" and using them for calculations.
+Resultierende Regenhöhe h_N(T_n=1.0a, D=720.0min) = 49.29 mm
+Resultierende Regenspende r_N(T_n=1.0a, D=720.0min) = 11.41 L/(s*ha)
+
+
+
+
[8]:
+
+
+
! python -m idf_analysis -i ehyd_112086.parquet -d 720 -h_N 60
+
+
+
+
+
+
+
+
+Using the subfolder "ehyd_112086_idf_data" for the interim- and final-results.
+Found existing interim-results in "ehyd_112086_idf_data\idf_parameters.yaml" and using them for calculations.
+The return period is 2.0 years.
+Resultierende Regenhöhe h_N(T_n=2.0a, D=720.0min) = 60.00 mm
+Resultierende Regenspende r_N(T_n=2.0a, D=720.0min) = 13.89 L/(s*ha)
+
+
+
+
[ ]:
+
+
+
! python -m idf_analysis -i ehyd_112086.parquet -t 5 -t 15
+
+
+
+
+
[5]:
+
+
+
! python -m idf_analysis -i ehyd_112086.parquet --plot
+
+
+
+
+
+
+
+
+Using the subfolder "ehyd_112086_idf_data" for the interim- and final-results.
+Found existing interim-results in "ehyd_112086_idf_data\idf_parameters.yaml" and using them for calculations.
+Created the IDF-curves-plot and saved the file as "ehyd_112086_idf_data\idf__curves_plot.png".
+
+
+
+
[6]:
+
+
+
! python -m idf_analysis -i ehyd_112086.parquet --export_table
+
+
+
+
+
+
+
+
+Using the subfolder "ehyd_112086_idf_data" for the interim- and final-results.
+Found existing interim-results in "ehyd_112086_idf_data\idf_parameters.yaml" and using them for calculations.
+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)
+5.0                 9.39   10.97   11.89   13.04   14.61   16.19   16.69   17.11   18.26   19.18   19.83
+10.0               15.15   17.62   19.06   20.88   23.35   25.82   26.62   27.27   29.09   30.54   31.56
+15.0               19.03   22.25   24.13   26.51   29.72   32.94   33.98   34.83   37.20   39.08   40.42
+20.0               21.83   25.71   27.99   30.85   34.73   38.62   39.87   40.89   43.75   46.02   47.63
+30.0               25.60   30.66   33.62   37.35   42.41   47.47   49.10   50.43   54.16   57.12   59.22
+45.0               28.92   35.51   39.37   44.23   50.83   57.42   59.54   61.28   66.14   69.99   72.73
+60.0               30.93   38.89   43.54   49.40   57.36   65.31   67.88   69.97   75.83   80.49   83.79
+90.0               33.37   41.74   46.64   52.80   61.17   69.54   72.23   74.43   80.60   85.49   88.96
+180.0              38.01   47.13   52.46   59.18   68.30   77.42   80.36   82.76   89.48   94.81   98.60
+270.0              41.01   50.60   56.21   63.28   72.87   82.46   85.55   88.07   95.14  100.75  104.73
+360.0              43.29   53.23   59.04   66.37   76.31   86.25   89.45   92.06   99.39  105.20  109.33
+450.0              45.14   55.36   61.33   68.87   79.08   89.30   92.59   95.28  102.81  108.79  113.03
+600.0              47.64   58.23   64.43   72.23   82.82   93.41   96.82   99.61  107.42  113.61  118.01
+720.0              49.29   60.13   66.47   74.45   85.29   96.12   99.61  102.46  110.44  116.78  121.28
+1080.0             54.41   64.97   71.15   78.94   89.50  100.06  103.46  106.24  114.02  120.20  124.58
+1440.0             58.02   67.72   73.39   80.54   90.24   99.93  103.05  105.61  112.75  118.42  122.45
+2880.0             66.70   77.41   83.68   91.57  102.29  113.00  116.45  119.26  127.16  133.42  137.87
+4320.0             71.93   85.72   93.78  103.95  117.73  131.52  135.96  139.58  149.75  157.81  163.53
+5760.0             78.95   95.65  105.42  117.72  134.43  151.13  156.50  160.89  173.20  182.97  189.90
+7200.0             83.53  101.38  111.82  124.98  142.83  160.68  166.43  171.12  184.28  194.72  202.13
+8640.0             85.38  104.95  116.40  130.82  150.38  169.95  176.25  181.40  195.82  207.27  215.39
+Created the IDF-curves-plot and saved the file as "ehyd_112086_idf_data\idf_table.csv".
+
+
+
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/examples/example_commandline.ipynb b/examples/example_commandline.ipynb new file mode 100644 index 0000000..5b06504 --- /dev/null +++ b/examples/example_commandline.ipynb @@ -0,0 +1,255 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Example Commandline Use" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "usage: __main__.py [-h] -i INPUT\n", + " [-ws {ATV-A_121,DWA-A_531,DWA-A_531_advektiv}]\n", + " [-kind {partial,annual}] [-t {>= 0.5 a and <= 100 a}]\n", + " [-d {>= 5 min and <= 8640 min}] [-r {>= 0 L/s*ha}]\n", + " [-h_N {>= 0 mm}] [--r_720_1] [--plot] [--export_table]\n", + "\n", + "heavy rain as a function of the duration and the return period acc. to DWA-A\n", + "531 (2012) All files will be saved in the same directory of the input file but\n", + "in a subfolder called like the inputfile + \"_idf_data\". Inside this folder a\n", + "file called \"idf_parameter.yaml\"-file will be saved and contains interim-\n", + "calculation-results and will be automatically reloaded on the next call.\n", + "\n", + "optional arguments:\n", + " -h, --help show this help message and exit\n", + " -i INPUT, --input INPUT\n", + " input file with the rain time-series (csv or parquet)\n", + " -ws {ATV-A_121,DWA-A_531,DWA-A_531_advektiv}, --worksheet {ATV-A_121,DWA-A_531,DWA-A_531_advektiv}\n", + " From which worksheet the recommendations for\n", + " calculating the parameters should be taken.\n", + " -kind {partial,annual}, --series_kind {partial,annual}\n", + " The kind of series used for the calculation.\n", + " (Calculation with partial series is more precise and\n", + " recommended.)\n", + " -t {>= 0.5 a and <= 100 a}, --return_period {>= 0.5 a and <= 100 a}\n", + " return period in years (If two of the three variables\n", + " (rainfall (height or flow-rate), duration, return\n", + " period) are given, the third variable is calculated.)\n", + " -d {>= 5 min and <= 8640 min}, --duration {>= 5 min and <= 8640 min}\n", + " duration in minutes (If two of the three variables\n", + " (rainfall (height or flow-rate), duration, return\n", + " period) are given, the third variable is calculated.)\n", + " -r {>= 0 L/(s*ha)}, --flow_rate_of_rainfall {>= 0 L/(s*ha)}\n", + " rainfall in Liter/(s * ha) (If two of the three\n", + " variables (rainfall (height or flow-rate), duration,\n", + " return period) are given, the third variable is\n", + " calculated.)\n", + " -h_N {>= 0 mm}, --height_of_rainfall {>= 0 mm}\n", + " rainfall in mm or Liter/m^2 (If two of the three\n", + " variables (rainfall (height or flow-rate), duration,\n", + " return period) are given, the third variable is\n", + " calculated.)\n", + " --r_720_1 design rainfall with a duration of 720 minutes (=12 h)\n", + " and a return period of 1 year\n", + " --plot get a plot of the idf relationship\n", + " --export_table get a table of the most frequent used values\n" + ] + } + ], + "source": [ + "! python -m idf_analysis -h" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I used the rain-time-series from ehyd.gv.at with the ID 112086 (Graz-Andritz)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using the subfolder \"ehyd_112086_idf_data\" for the interim- and final-results.\n", + "Found existing interim-results in \"ehyd_112086_idf_data\\idf_parameters.yaml\" and using them for calculations.\n" + ] + } + ], + "source": [ + "! python -m idf_analysis -i ehyd_112086.parquet" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using the subfolder \"ehyd_112086_idf_data\" for the interim- and final-results.\n", + "Found existing interim-results in \"ehyd_112086_idf_data\\idf_parameters.yaml\" and using them for calculations.\n", + "Resultierende Regenhöhe h_N(T_n=1.0a, D=720.0min) = 49.29 mm\n", + "Resultierende Regenspende r_N(T_n=1.0a, D=720.0min) = 11.41 L/(s*ha)\n" + ] + } + ], + "source": [ + "! python -m idf_analysis -i ehyd_112086.parquet --r_720_1" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using the subfolder \"ehyd_112086_idf_data\" for the interim- and final-results.\n", + "Found existing interim-results in \"ehyd_112086_idf_data\\idf_parameters.yaml\" and using them for calculations.\n", + "Resultierende Regenhöhe h_N(T_n=1.0a, D=720.0min) = 49.29 mm\n", + "Resultierende Regenspende r_N(T_n=1.0a, D=720.0min) = 11.41 L/(s*ha)\n" + ] + } + ], + "source": [ + "! python -m idf_analysis -i ehyd_112086.parquet -d 720 -t 1" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using the subfolder \"ehyd_112086_idf_data\" for the interim- and final-results.\n", + "Found existing interim-results in \"ehyd_112086_idf_data\\idf_parameters.yaml\" and using them for calculations.\n", + "The return period is 2.0 years.\n", + "Resultierende Regenhöhe h_N(T_n=2.0a, D=720.0min) = 60.00 mm\n", + "Resultierende Regenspende r_N(T_n=2.0a, D=720.0min) = 13.89 L/(s*ha)\n" + ] + } + ], + "source": [ + "! python -m idf_analysis -i ehyd_112086.parquet -d 720 -h_N 60" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "! python -m idf_analysis -i ehyd_112086.parquet -t 5 -t 15" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using the subfolder \"ehyd_112086_idf_data\" for the interim- and final-results.\n", + "Found existing interim-results in \"ehyd_112086_idf_data\\idf_parameters.yaml\" and using them for calculations.\n", + "Created the IDF-curves-plot and saved the file as \"ehyd_112086_idf_data\\idf__curves_plot.png\".\n" + ] + } + ], + "source": [ + "! python -m idf_analysis -i ehyd_112086.parquet --plot" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using the subfolder \"ehyd_112086_idf_data\" for the interim- and final-results.\n", + "Found existing interim-results in \"ehyd_112086_idf_data\\idf_parameters.yaml\" and using them for calculations.\n", + "return period (a) 1 2 3 5 10 20 25 30 50 75 100\n", + "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\n", + "duration (min) \n", + "5.0 9.39 10.97 11.89 13.04 14.61 16.19 16.69 17.11 18.26 19.18 19.83\n", + "10.0 15.15 17.62 19.06 20.88 23.35 25.82 26.62 27.27 29.09 30.54 31.56\n", + "15.0 19.03 22.25 24.13 26.51 29.72 32.94 33.98 34.83 37.20 39.08 40.42\n", + "20.0 21.83 25.71 27.99 30.85 34.73 38.62 39.87 40.89 43.75 46.02 47.63\n", + "30.0 25.60 30.66 33.62 37.35 42.41 47.47 49.10 50.43 54.16 57.12 59.22\n", + "45.0 28.92 35.51 39.37 44.23 50.83 57.42 59.54 61.28 66.14 69.99 72.73\n", + "60.0 30.93 38.89 43.54 49.40 57.36 65.31 67.88 69.97 75.83 80.49 83.79\n", + "90.0 33.37 41.74 46.64 52.80 61.17 69.54 72.23 74.43 80.60 85.49 88.96\n", + "180.0 38.01 47.13 52.46 59.18 68.30 77.42 80.36 82.76 89.48 94.81 98.60\n", + "270.0 41.01 50.60 56.21 63.28 72.87 82.46 85.55 88.07 95.14 100.75 104.73\n", + "360.0 43.29 53.23 59.04 66.37 76.31 86.25 89.45 92.06 99.39 105.20 109.33\n", + "450.0 45.14 55.36 61.33 68.87 79.08 89.30 92.59 95.28 102.81 108.79 113.03\n", + "600.0 47.64 58.23 64.43 72.23 82.82 93.41 96.82 99.61 107.42 113.61 118.01\n", + "720.0 49.29 60.13 66.47 74.45 85.29 96.12 99.61 102.46 110.44 116.78 121.28\n", + "1080.0 54.41 64.97 71.15 78.94 89.50 100.06 103.46 106.24 114.02 120.20 124.58\n", + "1440.0 58.02 67.72 73.39 80.54 90.24 99.93 103.05 105.61 112.75 118.42 122.45\n", + "2880.0 66.70 77.41 83.68 91.57 102.29 113.00 116.45 119.26 127.16 133.42 137.87\n", + "4320.0 71.93 85.72 93.78 103.95 117.73 131.52 135.96 139.58 149.75 157.81 163.53\n", + "5760.0 78.95 95.65 105.42 117.72 134.43 151.13 156.50 160.89 173.20 182.97 189.90\n", + "7200.0 83.53 101.38 111.82 124.98 142.83 160.68 166.43 171.12 184.28 194.72 202.13\n", + "8640.0 85.38 104.95 116.40 130.82 150.38 169.95 176.25 181.40 195.82 207.27 215.39\n", + "Created the IDF-curves-plot and saved the file as \"ehyd_112086_idf_data\\idf_table.csv\".\n" + ] + } + ], + "source": [ + "! python -m idf_analysis -i ehyd_112086.parquet --export_table" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/examples/example_heavy_rainfall_index.html b/examples/example_heavy_rainfall_index.html new file mode 100644 index 0000000..fb3fec7 --- /dev/null +++ b/examples/example_heavy_rainfall_index.html @@ -0,0 +1,2211 @@ + + + + + + + + Example for Heavy Rainfall Index — Intensity Duration Frequency Analyse 0.2.3 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+

Example for Heavy Rainfall Index

+

Based on the IntensityDurationFrequencyAnalysis, the HeavyRainIndexAnalysis enables the creation of location-dependent heavy rain indices according to the methods of Schmitt, Mudersbach and also those of Krüger and Pfister. Furthermore, the possibility of creating heavy rain index curves according to the Krüger and Pfister method was included and implemented for the other two methods. Thus, heavy rainfall index curves can be compared with each other. Furthermore, it is possible to assign +individual rain events to a heavy rain index using existing rain data.

+
+
[1]:
+
+
+
from matplotlib.colors import ListedColormap
+from idf_analysis.definitions import METHOD, SERIES, COL
+from idf_analysis.little_helpers import minutes_readable
+from idf_analysis.sww_utils import rain_events, event_duration, agg_events
+from idf_analysis.heavy_rainfall_index import HeavyRainfallIndexAnalyse
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from os import path
+
+%matplotlib inline
+plt.style.use('fast')
+
+
+
+
+
Implemented Methods for the HeavyRainIndexAnalysis:
+
SCHMITT = ‘Schmitt’
+
KRUEGER_PFISTER = ‘KruegerPfister’
+
MUDERSBACH = ‘Mudersbach’
+
+
+
[2]:
+
+
+
hri = HeavyRainfallIndexAnalyse(method=HeavyRainfallIndexAnalyse.METHODS.KRUEGER_PFISTER,
+                                series_kind=SERIES.PARTIAL, worksheet=METHOD.KOSTRA, extended_durations=True)
+
+
+
+
+
[3]:
+
+
+
cmap = ListedColormap([(1,1,1)] + list(hri.indices_color.values()))
+
+
+
+
+
[4]:
+
+
+
data = pd.read_parquet('ehyd_112086.parquet')
+
+
+
+
+
[5]:
+
+
+
output_directory = 'Ergebnisse'
+
+
+
+
+
[6]:
+
+
+
hri.set_series(data['precipitation'])
+
+
+
+
+
[7]:
+
+
+
hri.auto_save_parameters(path.join(output_directory, 'idf_parameters.yaml'))
+
+
+
+

Heavy rainfall index-matrix is created with regard to the individual procedures for SRI generation

+
+
[8]:
+
+
+
hri.interim_sri_table().style.background_gradient(cmap=cmap, vmin=1, vmax=12)
+
+
+
+
+
[8]:
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Return Period in a1235102025305075100
duration in min           
5.000001111111
10.000011111222
15.000011122223
20.000111222233
30.000111222334
45.001112233445
60.001112333455
90.001112334456
120.001112334556
180.001112344566
240.001122344566
360.001123444567
540.001123445677
720.001123445678
1080.001123455678
1440.001223455678
2880.011234556789
4320.0112346678910
5760.01123567891112
7200.012235788101112
8640.012235789101212
+
+

An auxiliary table must be generated for the creation of the heavy rain index-curves. Here, the rainfall heights are shown depending on the duration and the heavy rain index.

+
+
[9]:
+
+
+
hri.result_sri_table().style.background_gradient(cmap=cmap, vmin=0, vmax=700).format("{:.1f}")
+
+
+
+
+
[9]:
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
SRI123456789101112
duration in min            
5.017.925.330.935.739.943.847.350.553.656.559.261.9
10.022.531.939.145.150.455.259.763.867.671.374.878.1
15.025.536.144.251.157.162.567.672.276.680.784.788.5
20.027.739.248.055.562.067.973.478.583.287.792.096.1
30.031.043.853.661.969.275.881.987.692.997.9102.7107.2
45.034.348.659.568.776.884.190.897.1103.0108.6113.9118.9
60.036.952.263.973.882.590.397.6104.3110.6116.6122.3127.7
90.038.053.765.875.984.993.0100.4107.4113.9120.0125.9131.5
120.038.854.867.177.586.794.9102.5109.6116.3122.6128.5134.3
180.039.956.469.179.889.297.7105.6112.9119.7126.2132.3138.2
240.040.757.670.681.591.199.8107.8115.2122.2128.8135.1141.1
360.042.059.372.783.993.8102.8111.0118.7125.9132.7139.2145.3
540.043.261.174.886.496.6105.9114.3122.2129.6136.7143.3149.7
720.044.162.476.488.398.7108.1116.8124.8132.4139.6146.4152.9
1080.044.763.277.489.399.9109.4118.2126.4134.0141.3148.2154.8
1440.044.362.676.788.699.0108.5117.2125.3132.9140.1146.9153.4
2880.047.066.581.494.0105.1115.1124.3132.9141.0148.6155.9162.8
4320.051.272.488.7102.4114.4125.4135.4144.8153.5161.9169.8177.3
5760.055.278.095.5110.3123.3135.1145.9156.0165.5174.4182.9191.1
7200.056.980.598.6113.8127.2139.4150.6160.9170.7179.9188.7197.1
8640.058.783.1101.7117.5131.3143.9155.4166.1176.2185.8194.8203.5
+
+

Using heavy rainfall index curves, the necessary rainfall heights can be read off depending on the respective index.

+
+
[10]:
+
+
+
hri.method = hri.METHODS.KRUEGER_PFISTER
+fig, ax = hri.result_sri_figure()
+ax.legend(loc='upper left')
+ax.grid(color='grey', linestyle='-', linewidth=0.3)
+ax.set_ylim(0, 230)
+old_labels = ax.get_xticklabels()
+old_labels[2] = ''
+old_labels[5] = ''
+old_labels[19] = ''
+ax.set_xticklabels(old_labels)
+handles, labels = ax.get_legend_handles_labels()
+ax.legend(handles[::-1], labels[::-1], title='SRI', loc='upper left')
+plt.savefig("kruegerpfister.svg")
+
+
+
+
+
+
+
+../_images/examples_example_heavy_rainfall_index_15_0.png +
+
+

Heavy rain index allocation of a specific rain event

+
+
[11]:
+
+
+
events = rain_events(hri.series, min_gap=pd.Timedelta(days=2))
+events[COL.DUR] = event_duration(events)
+events[COL.LP] = agg_events(events, hri.series, 'sum').round(1)
+events[COL.LAST] = events[COL.START] - events[COL.END].shift()
+
+hri.add_max_return_periods_to_events(events)
+
+events = events[events[COL.LP] > 10]
+events = events[events[COL.MAX_PERIOD] > 2]
+
+events.sort_values(COL.LP)
+
+
+
+
+
+
+
+
+calculating rainfall_sum data-frame: 100%|██████████| 21/21 [00:00<00:00, 163.22it/s]
+calculating return_periods data-frame: 100%|██████████| 21/21 [00:00<00:00, 58.97it/s]
+
+
+
+
[11]:
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
startenddurationrain_sumlast_eventmax_return_periodmax_return_period_duration
402008-07-20 17:43:002008-07-21 08:57:000 days 15:14:0028.02 days 02:48:002.8310205760.0
362008-06-23 19:50:002008-06-24 19:48:000 days 23:58:0035.82 days 21:52:005.44279815.0
962009-08-27 19:52:002009-08-29 13:25:001 days 17:33:0055.14 days 23:06:0019.71872520.0
882009-07-18 08:55:002009-07-18 13:19:000 days 04:24:0058.02 days 01:56:003.379539240.0
952009-08-21 19:49:002009-08-22 20:46:001 days 00:57:0064.24 days 03:04:003.97573520.0
4332016-05-30 12:38:002016-06-06 12:55:007 days 00:17:0066.44 days 17:43:006.7937885.0
972009-09-04 00:31:002009-09-05 01:32:001 days 01:01:0069.85 days 11:06:002.6056741080.0
1932011-07-27 20:37:002011-08-04 07:45:007 days 11:08:0070.53 days 05:28:0012.73245220.0
3912015-07-08 11:50:002015-07-09 00:26:000 days 12:36:0077.88 days 13:52:005.981540720.0
3312014-05-07 16:49:002014-05-18 20:03:0011 days 03:14:00109.94 days 03:29:002.7988254320.0
3022013-08-24 01:33:002013-08-29 08:20:005 days 06:47:00114.19 days 12:51:003.5438552880.0
392008-07-12 22:10:002008-07-18 14:55:005 days 16:45:00122.82 days 11:12:006.296355120.0
2442012-07-09 18:49:002012-07-16 05:43:006 days 10:54:00123.15 days 23:07:003.7205308640.0
4412016-07-21 22:28:002016-07-28 14:14:006 days 15:46:00136.65 days 00:03:004.6637868640.0
2872013-05-02 02:45:002013-05-07 21:38:005 days 18:53:00148.29 days 05:06:0032.6024892880.0
+
+
+
+
[12]:
+
+
+
hri.add_max_return_periods_to_events(hri.rain_events)
+
+
+
+
+
[13]:
+
+
+
hri.rain_events.sort_values('max_return_period', ascending=False).head()
+
+
+
+
+
[13]:
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
startenddurationrain_sumlast_eventmax_return_periodmax_return_period_duration
7982013-05-07 20:57:002013-05-07 21:38:000 days 00:41:002.00 days 08:16:0032.6024892880.0
7972013-05-05 20:46:002013-05-07 12:41:001 days 15:55:00119.60 days 17:29:0030.5853042880.0
2892009-08-28 23:41:002009-08-29 00:42:000 days 01:01:0049.31 days 03:44:0019.71872520.0
5662011-08-03 19:52:002011-08-04 07:45:000 days 11:53:0054.71 days 23:09:0012.73245220.0
7992013-05-10 17:55:002013-05-10 22:21:000 days 04:26:003.92 days 20:17:007.4121518640.0
+
+
+
+
[14]:
+
+
+
event = hri.rain_events.loc[797]
+
+event[COL.START] = pd.to_datetime('2013-05-06 20:00:00')
+event
+
+
+
+
+
+
+
+
+/home/markus/.local/lib/python3.8/site-packages/pandas/core/series.py:1056: SettingWithCopyWarning:
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+  cacher_needs_updating = self._check_is_chained_assignment_possible()
+
+
+
+
[14]:
+
+
+
+
+start                         2013-05-06 20:00:00
+end                           2013-05-07 12:41:00
+duration                          1 days 15:55:00
+rain_sum                                    119.6
+last_event                        0 days 17:29:00
+max_return_period                       30.585304
+max_return_period_duration                 2880.0
+Name: 797, dtype: object
+
+
+
+
[15]:
+
+
+
rainfall_sum_frame = hri.rainfall_sum_frame[event[COL.START]:event[COL.END]]
+return_periods_frame = hri.return_periods_frame[event[COL.START]:event[COL.END]]
+
+
+
+
+
+
+
+
+calculating rainfall_sum data-frame: 100%|██████████| 21/21 [00:00<00:00, 171.82it/s]
+
+
+
+
[16]:
+
+
+
rainfall_sum_frame.\
+    max().\
+    rename('max. Regensumme').\
+    to_frame().\
+    rename(minutes_readable).\
+    style.bar(vmin=0, vmax=100).format("{:.1f}")
+
+
+
+
+
[16]:
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 max. Regensumme
5 min9.9
10 min16.3
15 min21.9
20 min28.2
30 min40.7
45 min46.3
60 min57.0
1.5 h69.6
2 h74.3
3 h76.5
4 h78.7
6 h89.6
9 h92.8
12 h93.0
18 h96.4
1 d98.2
2 d119.6
3 d136.0
4 d136.0
5 d143.2
6 d146.2
+
+
+
[17]:
+
+
+
return_periods_frame.\
+    max().\
+    rename('max. Wiederkehrperiode').\
+    to_frame().\
+    rename(minutes_readable).\
+    style.bar(vmin=0, vmax=100).format("{:.1f}")
+
+
+
+
+
[17]:
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 max. Wiederkehrperiode
5 min1.4
10 min1.5
15 min1.9
20 min3.2
30 min7.7
45 min6.0
60 min9.3
1.5 h19.1
2 h21.6
3 h17.9
4 h16.4
6 h24.6
9 h20.8
12 h16.2
18 h15.8
1 d17.7
2 d30.6
3 d25.0
4 d10.7
5 d10.1
6 d8.6
+
+
+
[18]:
+
+
+
sri_table_event = pd.DataFrame(index=hri.duration_steps)
+
+hri.method = hri.METHODS.KRUEGER_PFISTER
+sri_table_event[hri.METHODS.KRUEGER_PFISTER] = hri.get_event_sri_max(event[COL.START], event[COL.END])
+
+hri.method = hri.METHODS.MUDERSBACH
+sri_table_event[hri.METHODS.MUDERSBACH] = hri.get_event_sri_max(event[COL.START], event[COL.END])
+
+hri.method = hri.METHODS.SCHMITT
+sri_table_event[hri.METHODS.SCHMITT] = hri.get_event_sri_max(event[COL.START], event[COL.END])
+
+
+
+

Specific rain event with allocation of heavy rain indices depending on the method and duration.

+
+
[19]:
+
+
+
cmap = ListedColormap([(1,1,1)] + list(hri.indices_color.values()))
+_df = sri_table_event.astype(int).copy()
+_df['max. Wiederkehrperiode'] = return_periods_frame.max()
+_df['max. Regensumme'] = rainfall_sum_frame.max()
+_df.rename(minutes_readable).style.\
+    background_gradient(subset=[hri.METHODS.SCHMITT,
+                                hri.METHODS.KRUEGER_PFISTER,
+                                hri.METHODS.MUDERSBACH], cmap=cmap, vmin=0, vmax=12).\
+    bar(subset=['max. Wiederkehrperiode', 'max. Regensumme'], vmin=0, vmax=100).format("{:.1f}", subset=['max. Wiederkehrperiode', 'max. Regensumme'])
+
+
+
+
+
[19]:
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 KruegerPfisterMudersbachSchmittmax. Wiederkehrperiodemax. Regensumme
5 min1111.49.9
10 min1211.516.3
15 min1211.921.9
20 min1323.228.2
30 min2437.740.7
45 min2436.046.3
60 min2539.357.0
1.5 h36419.169.6
2 h47421.674.3
3 h46417.976.5
4 h46416.478.7
6 h47424.689.6
9 h47420.892.8
12 h47416.293.0
18 h47415.896.4
1 d57417.798.2
2 d68630.6119.6
3 d78525.0136.0
4 d57410.7136.0
5 d57410.1143.2
6 d5738.6146.2
+
+
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/examples/example_heavy_rainfall_index.ipynb b/examples/example_heavy_rainfall_index.ipynb new file mode 100644 index 0000000..be2fa7f --- /dev/null +++ b/examples/example_heavy_rainfall_index.ipynb @@ -0,0 +1,2315 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "# Example for Heavy Rainfall Index" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "Based on the IntensityDurationFrequencyAnalysis, the HeavyRainIndexAnalysis enables the creation of location-dependent heavy rain indices according to the methods of Schmitt, Mudersbach and also those of Krüger and Pfister.\n", + "Furthermore, the possibility of creating heavy rain index curves according to the Krüger and Pfister method was included and implemented for the other two methods. Thus, heavy rainfall index curves can be compared with each other. Furthermore, it is possible to assign individual rain events to a heavy rain index using existing rain data." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib.colors import ListedColormap\n", + "from idf_analysis.definitions import METHOD, SERIES, COL\n", + "from idf_analysis.little_helpers import minutes_readable\n", + "from idf_analysis.sww_utils import rain_events, event_duration, agg_events\n", + "from idf_analysis.heavy_rainfall_index import HeavyRainfallIndexAnalyse\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from os import path\n", + "\n", + "%matplotlib inline\n", + "plt.style.use('fast')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Implemented Methods for the HeavyRainIndexAnalysis: \n", + "SCHMITT = 'Schmitt' \n", + "KRUEGER_PFISTER = 'KruegerPfister' \n", + "MUDERSBACH = 'Mudersbach'" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "hri = HeavyRainfallIndexAnalyse(method=HeavyRainfallIndexAnalyse.METHODS.KRUEGER_PFISTER,\n", + " series_kind=SERIES.PARTIAL, worksheet=METHOD.KOSTRA, extended_durations=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "cmap = ListedColormap([(1,1,1)] + list(hri.indices_color.values()))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_parquet('ehyd_112086.parquet')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "output_directory = 'Ergebnisse'" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "hri.set_series(data['precipitation'])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "hri.auto_save_parameters(path.join(output_directory, 'idf_parameters.yaml'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Heavy rainfall index-matrix is created with regard to the individual procedures for SRI generation" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Return Period in a1235102025305075100
duration in min           
5.000001111111
10.000011111222
15.000011122223
20.000111222233
30.000111222334
45.001112233445
60.001112333455
90.001112334456
120.001112334556
180.001112344566
240.001122344566
360.001123444567
540.001123445677
720.001123445678
1080.001123455678
1440.001223455678
2880.011234556789
4320.0112346678910
5760.01123567891112
7200.012235788101112
8640.012235789101212
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hri.interim_sri_table().style.background_gradient(cmap=cmap, vmin=1, vmax=12)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An auxiliary table must be generated for the creation of the heavy rain index-curves. Here, the rainfall heights are shown depending on the duration and the heavy rain index." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
SRI123456789101112
duration in min            
5.017.925.330.935.739.943.847.350.553.656.559.261.9
10.022.531.939.145.150.455.259.763.867.671.374.878.1
15.025.536.144.251.157.162.567.672.276.680.784.788.5
20.027.739.248.055.562.067.973.478.583.287.792.096.1
30.031.043.853.661.969.275.881.987.692.997.9102.7107.2
45.034.348.659.568.776.884.190.897.1103.0108.6113.9118.9
60.036.952.263.973.882.590.397.6104.3110.6116.6122.3127.7
90.038.053.765.875.984.993.0100.4107.4113.9120.0125.9131.5
120.038.854.867.177.586.794.9102.5109.6116.3122.6128.5134.3
180.039.956.469.179.889.297.7105.6112.9119.7126.2132.3138.2
240.040.757.670.681.591.199.8107.8115.2122.2128.8135.1141.1
360.042.059.372.783.993.8102.8111.0118.7125.9132.7139.2145.3
540.043.261.174.886.496.6105.9114.3122.2129.6136.7143.3149.7
720.044.162.476.488.398.7108.1116.8124.8132.4139.6146.4152.9
1080.044.763.277.489.399.9109.4118.2126.4134.0141.3148.2154.8
1440.044.362.676.788.699.0108.5117.2125.3132.9140.1146.9153.4
2880.047.066.581.494.0105.1115.1124.3132.9141.0148.6155.9162.8
4320.051.272.488.7102.4114.4125.4135.4144.8153.5161.9169.8177.3
5760.055.278.095.5110.3123.3135.1145.9156.0165.5174.4182.9191.1
7200.056.980.598.6113.8127.2139.4150.6160.9170.7179.9188.7197.1
8640.058.783.1101.7117.5131.3143.9155.4166.1176.2185.8194.8203.5
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hri.result_sri_table().style.background_gradient(cmap=cmap, vmin=0, vmax=700).format(\"{:.1f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using heavy rainfall index curves, the necessary rainfall heights can be read off depending on the respective index." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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362008-06-23 19:50:002008-06-24 19:48:000 days 23:58:0035.82 days 21:52:005.44279815.0
962009-08-27 19:52:002009-08-29 13:25:001 days 17:33:0055.14 days 23:06:0019.71872520.0
882009-07-18 08:55:002009-07-18 13:19:000 days 04:24:0058.02 days 01:56:003.379539240.0
952009-08-21 19:49:002009-08-22 20:46:001 days 00:57:0064.24 days 03:04:003.97573520.0
4332016-05-30 12:38:002016-06-06 12:55:007 days 00:17:0066.44 days 17:43:006.7937885.0
972009-09-04 00:31:002009-09-05 01:32:001 days 01:01:0069.85 days 11:06:002.6056741080.0
1932011-07-27 20:37:002011-08-04 07:45:007 days 11:08:0070.53 days 05:28:0012.73245220.0
3912015-07-08 11:50:002015-07-09 00:26:000 days 12:36:0077.88 days 13:52:005.981540720.0
3312014-05-07 16:49:002014-05-18 20:03:0011 days 03:14:00109.94 days 03:29:002.7988254320.0
3022013-08-24 01:33:002013-08-29 08:20:005 days 06:47:00114.19 days 12:51:003.5438552880.0
392008-07-12 22:10:002008-07-18 14:55:005 days 16:45:00122.82 days 11:12:006.296355120.0
2442012-07-09 18:49:002012-07-16 05:43:006 days 10:54:00123.15 days 23:07:003.7205308640.0
4412016-07-21 22:28:002016-07-28 14:14:006 days 15:46:00136.65 days 00:03:004.6637868640.0
2872013-05-02 02:45:002013-05-07 21:38:005 days 18:53:00148.29 days 05:06:0032.6024892880.0
\n", + "
" + ], + "text/plain": [ + " start end duration rain_sum \\\n", + "40 2008-07-20 17:43:00 2008-07-21 08:57:00 0 days 15:14:00 28.0 \n", + "36 2008-06-23 19:50:00 2008-06-24 19:48:00 0 days 23:58:00 35.8 \n", + "96 2009-08-27 19:52:00 2009-08-29 13:25:00 1 days 17:33:00 55.1 \n", + "88 2009-07-18 08:55:00 2009-07-18 13:19:00 0 days 04:24:00 58.0 \n", + "95 2009-08-21 19:49:00 2009-08-22 20:46:00 1 days 00:57:00 64.2 \n", + "433 2016-05-30 12:38:00 2016-06-06 12:55:00 7 days 00:17:00 66.4 \n", + "97 2009-09-04 00:31:00 2009-09-05 01:32:00 1 days 01:01:00 69.8 \n", + "193 2011-07-27 20:37:00 2011-08-04 07:45:00 7 days 11:08:00 70.5 \n", + "391 2015-07-08 11:50:00 2015-07-09 00:26:00 0 days 12:36:00 77.8 \n", + "331 2014-05-07 16:49:00 2014-05-18 20:03:00 11 days 03:14:00 109.9 \n", + "302 2013-08-24 01:33:00 2013-08-29 08:20:00 5 days 06:47:00 114.1 \n", + "39 2008-07-12 22:10:00 2008-07-18 14:55:00 5 days 16:45:00 122.8 \n", + "244 2012-07-09 18:49:00 2012-07-16 05:43:00 6 days 10:54:00 123.1 \n", + "441 2016-07-21 22:28:00 2016-07-28 14:14:00 6 days 15:46:00 136.6 \n", + "287 2013-05-02 02:45:00 2013-05-07 21:38:00 5 days 18:53:00 148.2 \n", + "\n", + " last_event max_return_period max_return_period_duration \n", + "40 2 days 02:48:00 2.831020 5760.0 \n", + "36 2 days 21:52:00 5.442798 15.0 \n", + "96 4 days 23:06:00 19.718725 20.0 \n", + "88 2 days 01:56:00 3.379539 240.0 \n", + "95 4 days 03:04:00 3.975735 20.0 \n", + "433 4 days 17:43:00 6.793788 5.0 \n", + "97 5 days 11:06:00 2.605674 1080.0 \n", + "193 3 days 05:28:00 12.732452 20.0 \n", + "391 8 days 13:52:00 5.981540 720.0 \n", + "331 4 days 03:29:00 2.798825 4320.0 \n", + "302 9 days 12:51:00 3.543855 2880.0 \n", + "39 2 days 11:12:00 6.296355 120.0 \n", + "244 5 days 23:07:00 3.720530 8640.0 \n", + "441 5 days 00:03:00 4.663786 8640.0 \n", + "287 9 days 05:06:00 32.602489 2880.0 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "events = rain_events(hri.series, min_gap=pd.Timedelta(days=2))\n", + "events[COL.DUR] = event_duration(events)\n", + "events[COL.LP] = agg_events(events, hri.series, 'sum').round(1)\n", + "events[COL.LAST] = events[COL.START] - events[COL.END].shift()\n", + "\n", + "hri.add_max_return_periods_to_events(events)\n", + "\n", + "events = events[events[COL.LP] > 10]\n", + "events = events[events[COL.MAX_PERIOD] > 2]\n", + "\n", + "events.sort_values(COL.LP)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "hri.add_max_return_periods_to_events(hri.rain_events)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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startenddurationrain_sumlast_eventmax_return_periodmax_return_period_duration
7982013-05-07 20:57:002013-05-07 21:38:000 days 00:41:002.00 days 08:16:0032.6024892880.0
7972013-05-05 20:46:002013-05-07 12:41:001 days 15:55:00119.60 days 17:29:0030.5853042880.0
2892009-08-28 23:41:002009-08-29 00:42:000 days 01:01:0049.31 days 03:44:0019.71872520.0
5662011-08-03 19:52:002011-08-04 07:45:000 days 11:53:0054.71 days 23:09:0012.73245220.0
7992013-05-10 17:55:002013-05-10 22:21:000 days 04:26:003.92 days 20:17:007.4121518640.0
\n", + "
" + ], + "text/plain": [ + " start end duration rain_sum \\\n", + "798 2013-05-07 20:57:00 2013-05-07 21:38:00 0 days 00:41:00 2.0 \n", + "797 2013-05-05 20:46:00 2013-05-07 12:41:00 1 days 15:55:00 119.6 \n", + "289 2009-08-28 23:41:00 2009-08-29 00:42:00 0 days 01:01:00 49.3 \n", + "566 2011-08-03 19:52:00 2011-08-04 07:45:00 0 days 11:53:00 54.7 \n", + "799 2013-05-10 17:55:00 2013-05-10 22:21:00 0 days 04:26:00 3.9 \n", + "\n", + " last_event max_return_period max_return_period_duration \n", + "798 0 days 08:16:00 32.602489 2880.0 \n", + "797 0 days 17:29:00 30.585304 2880.0 \n", + "289 1 days 03:44:00 19.718725 20.0 \n", + "566 1 days 23:09:00 12.732452 20.0 \n", + "799 2 days 20:17:00 7.412151 8640.0 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hri.rain_events.sort_values('max_return_period', ascending=False).head()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/markus/.local/lib/python3.8/site-packages/pandas/core/series.py:1056: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " cacher_needs_updating = self._check_is_chained_assignment_possible()\n" + ] + }, + { + "data": { + "text/plain": [ + "start 2013-05-06 20:00:00\n", + "end 2013-05-07 12:41:00\n", + "duration 1 days 15:55:00\n", + "rain_sum 119.6\n", + "last_event 0 days 17:29:00\n", + "max_return_period 30.585304\n", + "max_return_period_duration 2880.0\n", + "Name: 797, dtype: object" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "event = hri.rain_events.loc[797]\n", + "\n", + "event[COL.START] = pd.to_datetime('2013-05-06 20:00:00')\n", + "event" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "calculating rainfall_sum data-frame: 100%|██████████| 21/21 [00:00<00:00, 171.82it/s]\n" + ] + } + ], + "source": [ + "rainfall_sum_frame = hri.rainfall_sum_frame[event[COL.START]:event[COL.END]]\n", + "return_periods_frame = hri.return_periods_frame[event[COL.START]:event[COL.END]]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 max. Regensumme
5 min9.9
10 min16.3
15 min21.9
20 min28.2
30 min40.7
45 min46.3
60 min57.0
1.5 h69.6
2 h74.3
3 h76.5
4 h78.7
6 h89.6
9 h92.8
12 h93.0
18 h96.4
1 d98.2
2 d119.6
3 d136.0
4 d136.0
5 d143.2
6 d146.2
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rainfall_sum_frame.\\\n", + " max().\\\n", + " rename('max. Regensumme').\\\n", + " to_frame().\\\n", + " rename(minutes_readable).\\\n", + " style.bar(vmin=0, vmax=100).format(\"{:.1f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 max. Wiederkehrperiode
5 min1.4
10 min1.5
15 min1.9
20 min3.2
30 min7.7
45 min6.0
60 min9.3
1.5 h19.1
2 h21.6
3 h17.9
4 h16.4
6 h24.6
9 h20.8
12 h16.2
18 h15.8
1 d17.7
2 d30.6
3 d25.0
4 d10.7
5 d10.1
6 d8.6
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "return_periods_frame.\\\n", + " max().\\\n", + " rename('max. Wiederkehrperiode').\\\n", + " to_frame().\\\n", + " rename(minutes_readable).\\\n", + " style.bar(vmin=0, vmax=100).format(\"{:.1f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "sri_table_event = pd.DataFrame(index=hri.duration_steps)\n", + "\n", + "hri.method = hri.METHODS.KRUEGER_PFISTER\n", + "sri_table_event[hri.METHODS.KRUEGER_PFISTER] = hri.get_event_sri_max(event[COL.START], event[COL.END])\n", + "\n", + "hri.method = hri.METHODS.MUDERSBACH\n", + "sri_table_event[hri.METHODS.MUDERSBACH] = hri.get_event_sri_max(event[COL.START], event[COL.END])\n", + "\n", + "hri.method = hri.METHODS.SCHMITT\n", + "sri_table_event[hri.METHODS.SCHMITT] = hri.get_event_sri_max(event[COL.START], event[COL.END])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Specific rain event with allocation of heavy rain indices depending on the method and duration. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 KruegerPfisterMudersbachSchmittmax. Wiederkehrperiodemax. Regensumme
5 min1111.49.9
10 min1211.516.3
15 min1211.921.9
20 min1323.228.2
30 min2437.740.7
45 min2436.046.3
60 min2539.357.0
1.5 h36419.169.6
2 h47421.674.3
3 h46417.976.5
4 h46416.478.7
6 h47424.689.6
9 h47420.892.8
12 h47416.293.0
18 h47415.896.4
1 d57417.798.2
2 d68630.6119.6
3 d78525.0136.0
4 d57410.7136.0
5 d57410.1143.2
6 d5738.6146.2
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cmap = ListedColormap([(1,1,1)] + list(hri.indices_color.values()))\n", + "_df = sri_table_event.astype(int).copy()\n", + "_df['max. Wiederkehrperiode'] = return_periods_frame.max()\n", + "_df['max. Regensumme'] = rainfall_sum_frame.max()\n", + "_df.rename(minutes_readable).style.\\\n", + " background_gradient(subset=[hri.METHODS.SCHMITT,\n", + " hri.METHODS.KRUEGER_PFISTER,\n", + " hri.METHODS.MUDERSBACH], cmap=cmap, vmin=0, vmax=12).\\\n", + " bar(subset=['max. Wiederkehrperiode', 'max. Regensumme'], vmin=0, vmax=100).format(\"{:.1f}\", subset=['max. Wiederkehrperiode', 'max. Regensumme'])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/example_python_api.html b/examples/example_python_api.html new file mode 100644 index 0000000..30d84bb --- /dev/null +++ b/examples/example_python_api.html @@ -0,0 +1,1246 @@ + + + + + + + + Intensity Duration Frequency Analyse — Intensity Duration Frequency Analyse 0.2.3 documentation + + + + + + + + + + + + + + + +
+
+
+
+ +
+
[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)

+
+
[3]:
+
+
+
data = pd.read_parquet('ehyd_112086.parquet')
+
+
+
+
+
[4]:
+
+
+
output_directory = 'ehyd_112086_idf_data'
+
+
+
+
+
[5]:
+
+
+
data.head()
+
+
+
+
+
[5]:
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
precipitation
datetime
2007-09-18 11:09:000.1
2007-09-18 11:12:000.1
2007-09-18 11:13:000.1
2007-09-18 11:14:000.1
2007-09-18 11:20:000.1
+
+
+
+
[6]:
+
+
+
data.tail()
+
+
+
+
+
[6]:
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
precipitation
datetime
2016-12-28 22:24:000.006
2016-12-28 22:25:000.092
2016-12-28 22:26:000.006
2016-12-28 22:45:000.090
2016-12-28 22:46:000.006
+
+
+
+
[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]:
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
1235102025305075100
59.3910.9711.8913.0414.6116.1916.6917.1118.2619.1819.83
1015.1517.6219.0620.8823.3525.8226.6227.2729.0930.5431.56
1519.0322.2524.1326.5129.7232.9433.9834.8337.2039.0840.42
2021.8325.7127.9930.8534.7338.6239.8740.8943.7546.0247.63
3025.6030.6633.6237.3542.4147.4749.1050.4354.1657.1259.22
4528.9235.5139.3744.2350.8357.4259.5461.2866.1469.9972.73
6030.9338.8943.5449.4057.3665.3167.8869.9775.8380.4983.79
9033.3741.7446.6452.8061.1769.5472.2374.4380.6085.4988.96
12035.2243.9048.9755.3664.0372.7075.4977.7884.1789.2492.84
18038.0147.1352.4659.1868.3077.4280.3682.7689.4894.8198.60
24040.1249.5755.1062.0671.5180.9784.0186.4993.4698.99102.91
36043.2953.2359.0466.3776.3186.2589.4592.0699.39105.20109.33
54046.7157.1663.2770.9881.4391.8995.2598.00105.71111.82116.16
72049.2960.1366.4774.4585.2996.1299.61102.46110.44116.78121.28
108054.4164.9771.1578.9489.50100.06103.46106.24114.02120.20124.58
144058.0267.7273.3980.5490.2499.93103.05105.61112.75118.42122.45
288066.7077.4183.6891.57102.29113.00116.45119.26127.16133.42137.87
432071.9385.7293.78103.95117.73131.52135.96139.58149.75157.81163.53
576078.9595.65105.42117.72134.43151.13156.50160.89173.20182.97189.90
720083.53101.38111.82124.98142.83160.68166.43171.12184.28194.72202.13
864085.38104.95116.40130.82150.38169.95176.25181.40195.82207.27215.39
+
+
+
+
[18]:
+
+
+
idf.result_table(add_names=True).round(2)
+
+
+
+
+
[18]:
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
return period (a)1235102025305075100
frequency (1/a)1.0000.5000.3330.2000.1000.0500.0400.0330.0200.0130.010
duration (min)
59.3910.9711.8913.0414.6116.1916.6917.1118.2619.1819.83
1015.1517.6219.0620.8823.3525.8226.6227.2729.0930.5431.56
1519.0322.2524.1326.5129.7232.9433.9834.8337.2039.0840.42
2021.8325.7127.9930.8534.7338.6239.8740.8943.7546.0247.63
3025.6030.6633.6237.3542.4147.4749.1050.4354.1657.1259.22
4528.9235.5139.3744.2350.8357.4259.5461.2866.1469.9972.73
6030.9338.8943.5449.4057.3665.3167.8869.9775.8380.4983.79
9033.3741.7446.6452.8061.1769.5472.2374.4380.6085.4988.96
12035.2243.9048.9755.3664.0372.7075.4977.7884.1789.2492.84
18038.0147.1352.4659.1868.3077.4280.3682.7689.4894.8198.60
24040.1249.5755.1062.0671.5180.9784.0186.4993.4698.99102.91
36043.2953.2359.0466.3776.3186.2589.4592.0699.39105.20109.33
54046.7157.1663.2770.9881.4391.8995.2598.00105.71111.82116.16
72049.2960.1366.4774.4585.2996.1299.61102.46110.44116.78121.28
108054.4164.9771.1578.9489.50100.06103.46106.24114.02120.20124.58
144058.0267.7273.3980.5490.2499.93103.05105.61112.75118.42122.45
288066.7077.4183.6891.57102.29113.00116.45119.26127.16133.42137.87
432071.9385.7293.78103.95117.73131.52135.96139.58149.75157.81163.53
576078.9595.65105.42117.72134.43151.13156.50160.89173.20182.97189.90
720083.53101.38111.82124.98142.83160.68166.43171.12184.28194.72202.13
864085.38104.95116.40130.82150.38169.95176.25181.40195.82207.27215.39
+
+
+

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 +
+
+
+
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/examples/example_python_api.ipynb b/examples/example_python_api.ipynb new file mode 100644 index 0000000..035788e --- /dev/null +++ b/examples/example_python_api.ipynb @@ -0,0 +1,1454 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "from idf_analysis.idf_class import IntensityDurationFrequencyAnalyse\n", + "from idf_analysis.definitions import *\n", + "import pandas as pd\n", + "from os import path\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "plt.style.use('bmh')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Intensity Duration Frequency Analyse" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parameter\n", + "\n", + "**series_kind**:\n", + "\n", + "`SERIES.PARTIAL` = Partielle Serie (partial duration series, PDS) (peak over threshold, POT)\n", + "\n", + "`SERIES.ANNUAL` = Jährliche Serie (annual maximum series, AMS)\n", + "\n", + "**worksheet**:\n", + "\n", + "`METHOD.KOSTRA`:\n", + "- DWA-A 531\n", + "- KOSTRA - empfohlen\n", + "- Stützstellen: 60 min und 12 h\n", + "\n", + "`METHOD.CONVECTIVE_ADVECTIVE`:\n", + "- DWA-A 531\n", + "- Unterscheidung in überwiegend konvektiv und advektiv verursachte Starkregen\n", + "- Stützstellen: 3 h und 24 h\n", + "\n", + "`METHOD.ATV`:\n", + "- ATV-A 121\n", + "- Stützstellen: 3 h und 48 h\n", + "\n", + "**extended_durations** = Inkludiert die Dauerstufen `[0.75d, 1d, 2d, 3d, 4d, 5d, 6d]` in der Analyse (in d=Tage)\n", + "\n", + "Standardmäßig berechnete Dauerstufen `[5m, 10m, 15m, 20m, 30m, 45m, 60m, 1.5h, 3h, 4.5h, 6h, 7.5h, 10h, 12h]`" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "idf = IntensityDurationFrequencyAnalyse(series_kind=SERIES.PARTIAL, worksheet=METHOD.KOSTRA, extended_durations=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I used the rain-time-series from ehyd.gv.at with the ID 112086 (Graz-Andritz)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "data = pd.read_parquet('ehyd_112086.parquet')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "output_directory = 'ehyd_112086_idf_data'" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
precipitation
datetime
2007-09-18 11:09:000.1
2007-09-18 11:12:000.1
2007-09-18 11:13:000.1
2007-09-18 11:14:000.1
2007-09-18 11:20:000.1
\n", + "
" + ], + "text/plain": [ + " precipitation\n", + "datetime \n", + "2007-09-18 11:09:00 0.1\n", + "2007-09-18 11:12:00 0.1\n", + "2007-09-18 11:13:00 0.1\n", + "2007-09-18 11:14:00 0.1\n", + "2007-09-18 11:20:00 0.1" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
precipitation
datetime
2016-12-28 22:24:000.006
2016-12-28 22:25:000.092
2016-12-28 22:26:000.006
2016-12-28 22:45:000.090
2016-12-28 22:46:000.006
\n", + "
" + ], + "text/plain": [ + " precipitation\n", + "datetime \n", + "2016-12-28 22:24:00 0.006\n", + "2016-12-28 22:25:00 0.092\n", + "2016-12-28 22:26:00 0.006\n", + "2016-12-28 22:45:00 0.090\n", + "2016-12-28 22:46:00 0.006" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "idf.set_series(data['precipitation'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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.\n", + "Auserdem enthalten diese Zwischenergebnisse die Parameter, die zur Berechnung der Regenhöhe und Regenspende benötigt werden.\n", + "Hier sind bereist die Berechnungsverfahren und Stückpunkte laut dem gewählten `worksheet` berücksichtigt." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "idf.auto_save_parameters(path.join(output_directory, 'idf_parameters.yaml'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Abgerufen können diese Zwischenergebnisse mit:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idf.parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Berechnungen" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "19.031596336052708" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idf.depth_of_rainfall(duration=15, return_period=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Resultierende Regenhöhe h_N(T_n=1.0a, D=15.0min) = 19.03 mm\n" + ] + } + ], + "source": [ + "print('Resultierende Regenhöhe h_N(T_n={t:0.1f}a, D={d:0.1f}min) = {h:0.2f} mm'\n", + " ''.format(t=1, d=15, h=idf.depth_of_rainfall(15, 1)))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "211.46218151169674" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idf.rain_flow_rate(duration=15, return_period=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Resultierende Regenspende r_N(T_n=1.0a, D=15.0min) = 211.46 L/(s*ha)\n" + ] + } + ], + "source": [ + "print('Resultierende Regenspende r_N(T_n={t:0.1f}a, D={d:0.1f}min) = {r:0.2f} L/(s*ha)'\n", + " ''.format(t=1, d=15, r=idf.rain_flow_rate(15, 1)))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "11.410836729727" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idf.r_720_1()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.1430180144131331" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idf.get_return_period(height_of_rainfall=10, duration=15)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "5.433080747189968" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idf.get_duration(height_of_rainfall=10, return_period=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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24040.1249.5755.1062.0671.5180.9784.0186.4993.4698.99102.91
36043.2953.2359.0466.3776.3186.2589.4592.0699.39105.20109.33
54046.7157.1663.2770.9881.4391.8995.2598.00105.71111.82116.16
72049.2960.1366.4774.4585.2996.1299.61102.46110.44116.78121.28
108054.4164.9771.1578.9489.50100.06103.46106.24114.02120.20124.58
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288066.7077.4183.6891.57102.29113.00116.45119.26127.16133.42137.87
432071.9385.7293.78103.95117.73131.52135.96139.58149.75157.81163.53
576078.9595.65105.42117.72134.43151.13156.50160.89173.20182.97189.90
720083.53101.38111.82124.98142.83160.68166.43171.12184.28194.72202.13
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return period (a)1235102025305075100
frequency (1/a)1.0000.5000.3330.2000.1000.0500.0400.0330.0200.0130.010
duration (min)
59.3910.9711.8913.0414.6116.1916.6917.1118.2619.1819.83
1015.1517.6219.0620.8823.3525.8226.6227.2729.0930.5431.56
1519.0322.2524.1326.5129.7232.9433.9834.8337.2039.0840.42
2021.8325.7127.9930.8534.7338.6239.8740.8943.7546.0247.63
3025.6030.6633.6237.3542.4147.4749.1050.4354.1657.1259.22
4528.9235.5139.3744.2350.8357.4259.5461.2866.1469.9972.73
6030.9338.8943.5449.4057.3665.3167.8869.9775.8380.4983.79
9033.3741.7446.6452.8061.1769.5472.2374.4380.6085.4988.96
12035.2243.9048.9755.3664.0372.7075.4977.7884.1789.2492.84
18038.0147.1352.4659.1868.3077.4280.3682.7689.4894.8198.60
24040.1249.5755.1062.0671.5180.9784.0186.4993.4698.99102.91
36043.2953.2359.0466.3776.3186.2589.4592.0699.39105.20109.33
54046.7157.1663.2770.9881.4391.8995.2598.00105.71111.82116.16
72049.2960.1366.4774.4585.2996.1299.61102.46110.44116.78121.28
108054.4164.9771.1578.9489.50100.06103.46106.24114.02120.20124.58
144058.0267.7273.3980.5490.2499.93103.05105.61112.75118.42122.45
288066.7077.4183.6891.57102.29113.00116.45119.26127.16133.42137.87
432071.9385.7293.78103.95117.73131.52135.96139.58149.75157.81163.53
576078.9595.65105.42117.72134.43151.13156.50160.89173.20182.97189.90
720083.53101.38111.82124.98142.83160.68166.43171.12184.28194.72202.13
864085.38104.95116.40130.82150.38169.95176.25181.40195.82207.27215.39
\n", + "
" + ], + "text/plain": [ + "return period (a) 1 2 3 5 10 20 25 \\\n", + "frequency (1/a) 1.000 0.500 0.333 0.200 0.100 0.050 0.040 \n", + "duration (min) \n", + "5 9.39 10.97 11.89 13.04 14.61 16.19 16.69 \n", + "10 15.15 17.62 19.06 20.88 23.35 25.82 26.62 \n", + "15 19.03 22.25 24.13 26.51 29.72 32.94 33.98 \n", + "20 21.83 25.71 27.99 30.85 34.73 38.62 39.87 \n", + "30 25.60 30.66 33.62 37.35 42.41 47.47 49.10 \n", + "45 28.92 35.51 39.37 44.23 50.83 57.42 59.54 \n", + "60 30.93 38.89 43.54 49.40 57.36 65.31 67.88 \n", + "90 33.37 41.74 46.64 52.80 61.17 69.54 72.23 \n", + "120 35.22 43.90 48.97 55.36 64.03 72.70 75.49 \n", + "180 38.01 47.13 52.46 59.18 68.30 77.42 80.36 \n", + "240 40.12 49.57 55.10 62.06 71.51 80.97 84.01 \n", + "360 43.29 53.23 59.04 66.37 76.31 86.25 89.45 \n", + "540 46.71 57.16 63.27 70.98 81.43 91.89 95.25 \n", + "720 49.29 60.13 66.47 74.45 85.29 96.12 99.61 \n", + "1080 54.41 64.97 71.15 78.94 89.50 100.06 103.46 \n", + "1440 58.02 67.72 73.39 80.54 90.24 99.93 103.05 \n", + "2880 66.70 77.41 83.68 91.57 102.29 113.00 116.45 \n", + "4320 71.93 85.72 93.78 103.95 117.73 131.52 135.96 \n", + "5760 78.95 95.65 105.42 117.72 134.43 151.13 156.50 \n", + "7200 83.53 101.38 111.82 124.98 142.83 160.68 166.43 \n", + "8640 85.38 104.95 116.40 130.82 150.38 169.95 176.25 \n", + "\n", + "return period (a) 30 50 75 100 \n", + "frequency (1/a) 0.033 0.020 0.013 0.010 \n", + "duration (min) \n", + "5 17.11 18.26 19.18 19.83 \n", + "10 27.27 29.09 30.54 31.56 \n", + "15 34.83 37.20 39.08 40.42 \n", + "20 40.89 43.75 46.02 47.63 \n", + "30 50.43 54.16 57.12 59.22 \n", + "45 61.28 66.14 69.99 72.73 \n", + "60 69.97 75.83 80.49 83.79 \n", + "90 74.43 80.60 85.49 88.96 \n", + "120 77.78 84.17 89.24 92.84 \n", + "180 82.76 89.48 94.81 98.60 \n", + "240 86.49 93.46 98.99 102.91 \n", + "360 92.06 99.39 105.20 109.33 \n", + "540 98.00 105.71 111.82 116.16 \n", + "720 102.46 110.44 116.78 121.28 \n", + "1080 106.24 114.02 120.20 124.58 \n", + "1440 105.61 112.75 118.42 122.45 \n", + "2880 119.26 127.16 133.42 137.87 \n", + "4320 139.58 149.75 157.81 163.53 \n", + "5760 160.89 173.20 182.97 189.90 \n", + "7200 171.12 184.28 194.72 202.13 \n", + "8640 181.40 195.82 207.27 215.39 " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idf.result_table(add_names=True).round(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "To save the table as a csv:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "idf.result_table(add_names=True).round(2).to_csv(path.join(output_directory, 'idf_table_UNIX.csv'), sep=',', decimal='.', float_format='%0.2f')" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "return period (a) 1 2 3 5 10 20 25 30 50 75 100\n", + "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\n", + "duration (min) \n", + "5 9.39 10.97 11.89 13.04 14.61 16.19 16.69 17.11 18.26 19.18 19.83\n", + "10 15.15 17.62 19.06 20.88 23.35 25.82 26.62 27.27 29.09 30.54 31.56\n", + "15 19.03 22.25 24.13 26.51 29.72 32.94 33.98 34.83 37.20 39.08 40.42\n", + "20 21.83 25.71 27.99 30.85 34.73 38.62 39.87 40.89 43.75 46.02 47.63\n", + "30 25.60 30.66 33.62 37.35 42.41 47.47 49.10 50.43 54.16 57.12 59.22\n", + "45 28.92 35.51 39.37 44.23 50.83 57.42 59.54 61.28 66.14 69.99 72.73\n", + "60 30.93 38.89 43.54 49.40 57.36 65.31 67.88 69.97 75.83 80.49 83.79\n", + "90 33.37 41.74 46.64 52.80 61.17 69.54 72.23 74.43 80.60 85.49 88.96\n", + "120 35.22 43.90 48.97 55.36 64.03 72.70 75.49 77.78 84.17 89.24 92.84\n", + "180 38.01 47.13 52.46 59.18 68.30 77.42 80.36 82.76 89.48 94.81 98.60\n", + "240 40.12 49.57 55.10 62.06 71.51 80.97 84.01 86.49 93.46 98.99 102.91\n", + "360 43.29 53.23 59.04 66.37 76.31 86.25 89.45 92.06 99.39 105.20 109.33\n", + "540 46.71 57.16 63.27 70.98 81.43 91.89 95.25 98.00 105.71 111.82 116.16\n", + "720 49.29 60.13 66.47 74.45 85.29 96.12 99.61 102.46 110.44 116.78 121.28\n", + "1080 54.41 64.97 71.15 78.94 89.50 100.06 103.46 106.24 114.02 120.20 124.58\n", + "1440 58.02 67.72 73.39 80.54 90.24 99.93 103.05 105.61 112.75 118.42 122.45\n", + "2880 66.70 77.41 83.68 91.57 102.29 113.00 116.45 119.26 127.16 133.42 137.87\n", + "4320 71.93 85.72 93.78 103.95 117.73 131.52 135.96 139.58 149.75 157.81 163.53\n", + "5760 78.95 95.65 105.42 117.72 134.43 151.13 156.50 160.89 173.20 182.97 189.90\n", + "7200 83.53 101.38 111.82 124.98 142.83 160.68 166.43 171.12 184.28 194.72 202.13\n", + "8640 85.38 104.95 116.40 130.82 150.38 169.95 176.25 181.40 195.82 207.27 215.39\n" + ] + } + ], + "source": [ + "print(idf.result_table(add_names=True).round(2).to_string())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To create a color plot of the IDF curves:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = idf.result_figure(color=True, add_interim=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To create a black/white plot of the IDF curves:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = idf.result_figure(color=False, add_interim=True)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/examples/example_python_api_extended.html b/examples/example_python_api_extended.html new file mode 100644 index 0000000..6697c25 --- /dev/null +++ b/examples/example_python_api_extended.html @@ -0,0 +1,519 @@ + + + + + + + + Intensity Duration Frequency Analyse - EXTENDED — Intensity Duration Frequency Analyse 0.2.3 documentation + + + + + + + + + + + + + + +
+
+
+
+ +
+
[2]:
+
+
+
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 - EXTENDED

+
+
[3]:
+
+
+
# sub-folder for the results
+output_directory = path.join('ehyd_112086_idf_data')
+
+# initialize of the analysis class
+idf = IntensityDurationFrequencyAnalyse(series_kind=SERIES.PARTIAL, worksheet=METHOD.KOSTRA, extended_durations=True)
+
+# reading the pandas series of the precipitation (data from ehyd.gv.at - ID=112086)
+series = pd.read_parquet('ehyd_112086.parquet')['precipitation']
+
+# setting the series for the analysis
+idf.set_series(series)
+
+# auto-save the calculated parameter so save time for a later use
+idf.auto_save_parameters(path.join(output_directory, 'idf_parameters.yaml'))
+
+
+
+
+
[12]:
+
+
+
events = idf.rain_events
+"Columns: ", events.columns,  "| Number of events: ", events.index.size
+
+
+
+
+
[12]:
+
+
+
+
+('Columns: ',
+ Index(['start', 'end', 'duration', 'rain_sum', 'last_event'], dtype='object'),
+ '| Number of events: ',
+ 1356)
+
+
+
+
[13]:
+
+
+
# reduce number of event by limiting the minimum sum of rainfall
+events = events[events[COL.LP] > 10].copy()
+"Columns: ", events.columns,  "| Number of events: ", events.index.size
+
+
+
+
+
[13]:
+
+
+
+
+('Columns: ',
+ Index(['start', 'end', 'duration', 'rain_sum', 'last_event'], dtype='object'),
+ '| Number of events: ',
+ 252)
+
+
+
+
[14]:
+
+
+
# add the maximum return period to the events and at which duration this period occurs
+idf.add_max_return_periods_to_events(events)
+"Columns: ", events.columns,  "| Number of events: ", events.index.size
+
+
+
+
+
[14]:
+
+
+
+
+('Columns: ',
+ Index(['start', 'end', 'duration', 'rain_sum', 'last_event',
+        'max_return_period', 'max_return_period_duration'],
+       dtype='object'),
+ '| Number of events: ',
+ 252)
+
+
+
+
[15]:
+
+
+
# reduce number of event by limiting the minimum return period of an event
+events = events[events[COL.MAX_PERIOD] > 2].copy()
+"Columns: ", events.columns,  "| Number of events: ", events.index.size
+
+
+
+
+
[15]:
+
+
+
+
+('Columns: ',
+ Index(['start', 'end', 'duration', 'rain_sum', 'last_event',
+        'max_return_period', 'max_return_period_duration'],
+       dtype='object'),
+ '| Number of events: ',
+ 19)
+
+
+
+
[16]:
+
+
+
events
+
+
+
+
+
[16]:
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
startenddurationrain_sumlast_eventmax_return_periodmax_return_period_duration
1052008-06-23 19:50:002008-06-23 23:23:000 days 03:33:0034.92 days 21:52:005.36378815.0
1252008-07-17 14:14:002008-07-18 06:27:000 days 16:13:0074.60 days 04:09:006.457449120.0
1272008-07-20 17:43:002008-07-21 03:55:000 days 10:12:0027.72 days 02:48:002.8310135760.0
2772009-07-18 08:55:002009-07-18 13:19:000 days 04:24:0058.02 days 01:56:003.422038240.0
2862009-08-21 19:49:002009-08-21 20:29:000 days 00:40:0034.24 days 03:04:003.93016120.0
2892009-08-28 23:41:002009-08-29 00:42:000 days 01:01:0049.31 days 03:44:0020.29909320.0
2912009-09-04 00:31:002009-09-04 18:18:000 days 17:47:0069.05 days 11:06:002.6056591080.0
5662011-08-03 19:52:002011-08-04 07:45:000 days 11:53:0054.71 days 23:09:0012.96286620.0
6842012-07-14 16:54:002012-07-15 13:07:000 days 20:13:0059.81 days 01:15:003.7205318640.0
7972013-05-05 20:46:002013-05-07 12:41:001 days 15:55:00119.60 days 17:29:0030.5849812880.0
8442013-08-27 17:09:002013-08-27 23:48:000 days 06:39:0043.30 days 13:56:002.4520995.0
8452013-08-28 11:41:002013-08-28 16:22:000 days 04:41:0028.50 days 11:53:003.5438372880.0
9472014-05-11 10:20:002014-05-11 23:18:000 days 12:58:0055.70 days 13:15:002.0821952880.0
9482014-05-12 17:22:002014-05-13 17:27:001 days 00:05:0020.60 days 18:04:002.7988224320.0
11372015-07-08 11:50:002015-07-09 00:26:000 days 12:36:0077.88 days 13:52:006.054031720.0
12612016-06-05 11:03:002016-06-05 14:57:000 days 03:54:0040.40 days 19:55:006.5371125.0
12942016-07-21 22:28:002016-07-21 23:21:000 days 00:53:0037.25 days 00:03:002.42662330.0
12982016-07-25 10:18:002016-07-25 15:48:000 days 05:30:0033.80 days 19:00:003.4003475760.0
13002016-07-27 15:25:002016-07-27 20:25:000 days 05:00:0012.21 days 01:04:004.6637878640.0
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[18]:
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+
# lets pick one event
+event = events.loc[125]
+event
+

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[18]:
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+start                         2008-07-17 14:14:00
+end                           2008-07-18 06:27:00
+duration                          0 days 16:13:00
+rain_sum                                     74.6
+last_event                        0 days 04:09:00
+max_return_period                        6.457449
+max_return_period_duration                  120.0
+Name: 125, dtype: object
+
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[19]:
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fig, caption = idf.event_plot(event)
+fig.tight_layout()
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+../_images/examples_example_python_api_extended_9_1.png +
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[20]:
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+
# you can also reduce the displayed duration steps
+fig, caption = idf.event_plot(event, durations=idf.duration_steps[:11])
+fig.tight_layout()
+
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+../_images/examples_example_python_api_extended_10_0.png +
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+ + + + \ No newline at end of file diff --git a/examples/example_python_api_extended.ipynb b/examples/example_python_api_extended.ipynb new file mode 100644 index 0000000..e4161d6 --- /dev/null +++ b/examples/example_python_api_extended.ipynb @@ -0,0 +1,589 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from idf_analysis.idf_class import IntensityDurationFrequencyAnalyse\n", + "from idf_analysis.definitions import *\n", + "import pandas as pd\n", + "from os import path\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "plt.style.use('bmh')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "# Intensity Duration Frequency Analyse - EXTENDED" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# sub-folder for the results\n", + "output_directory = path.join('ehyd_112086_idf_data')\n", + "\n", + "# initialize of the analysis class\n", + "idf = IntensityDurationFrequencyAnalyse(series_kind=SERIES.PARTIAL, worksheet=METHOD.KOSTRA, extended_durations=True)\n", + "\n", + "# reading the pandas series of the precipitation (data from ehyd.gv.at - ID=112086)\n", + "series = pd.read_parquet('ehyd_112086.parquet')['precipitation']\n", + "\n", + "# setting the series for the analysis\n", + "idf.set_series(series)\n", + "\n", + "# auto-save the calculated parameter so save time for a later use\n", + "idf.auto_save_parameters(path.join(output_directory, 'idf_parameters.yaml'))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "('Columns: ',\n", + " Index(['start', 'end', 'duration', 'rain_sum', 'last_event'], dtype='object'),\n", + " '| Number of events: ',\n", + " 1356)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "events = idf.rain_events\n", + "\"Columns: \", events.columns, \"| Number of events: \", events.index.size" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "('Columns: ',\n", + " Index(['start', 'end', 'duration', 'rain_sum', 'last_event'], dtype='object'),\n", + " '| Number of events: ',\n", + " 252)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# reduce number of event by limiting the minimum sum of rainfall\n", + "events = events[events[COL.LP] > 10].copy()\n", + "\"Columns: \", events.columns, \"| Number of events: \", events.index.size" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "('Columns: ',\n", + " Index(['start', 'end', 'duration', 'rain_sum', 'last_event',\n", + " 'max_return_period', 'max_return_period_duration'],\n", + " dtype='object'),\n", + " '| Number of events: ',\n", + " 252)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# add the maximum return period to the events and at which duration this period occurs\n", + "idf.add_max_return_periods_to_events(events)\n", + "\"Columns: \", events.columns, \"| Number of events: \", events.index.size" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "('Columns: ',\n", + " Index(['start', 'end', 'duration', 'rain_sum', 'last_event',\n", + " 'max_return_period', 'max_return_period_duration'],\n", + " dtype='object'),\n", + " '| Number of events: ',\n", + " 19)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# reduce number of event by limiting the minimum return period of an event\n", + "events = events[events[COL.MAX_PERIOD] > 2].copy()\n", + "\"Columns: \", events.columns, \"| Number of events: \", events.index.size" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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startenddurationrain_sumlast_eventmax_return_periodmax_return_period_duration
1052008-06-23 19:50:002008-06-23 23:23:000 days 03:33:0034.92 days 21:52:005.36378815.0
1252008-07-17 14:14:002008-07-18 06:27:000 days 16:13:0074.60 days 04:09:006.457449120.0
1272008-07-20 17:43:002008-07-21 03:55:000 days 10:12:0027.72 days 02:48:002.8310135760.0
2772009-07-18 08:55:002009-07-18 13:19:000 days 04:24:0058.02 days 01:56:003.422038240.0
2862009-08-21 19:49:002009-08-21 20:29:000 days 00:40:0034.24 days 03:04:003.93016120.0
2892009-08-28 23:41:002009-08-29 00:42:000 days 01:01:0049.31 days 03:44:0020.29909320.0
2912009-09-04 00:31:002009-09-04 18:18:000 days 17:47:0069.05 days 11:06:002.6056591080.0
5662011-08-03 19:52:002011-08-04 07:45:000 days 11:53:0054.71 days 23:09:0012.96286620.0
6842012-07-14 16:54:002012-07-15 13:07:000 days 20:13:0059.81 days 01:15:003.7205318640.0
7972013-05-05 20:46:002013-05-07 12:41:001 days 15:55:00119.60 days 17:29:0030.5849812880.0
8442013-08-27 17:09:002013-08-27 23:48:000 days 06:39:0043.30 days 13:56:002.4520995.0
8452013-08-28 11:41:002013-08-28 16:22:000 days 04:41:0028.50 days 11:53:003.5438372880.0
9472014-05-11 10:20:002014-05-11 23:18:000 days 12:58:0055.70 days 13:15:002.0821952880.0
9482014-05-12 17:22:002014-05-13 17:27:001 days 00:05:0020.60 days 18:04:002.7988224320.0
11372015-07-08 11:50:002015-07-09 00:26:000 days 12:36:0077.88 days 13:52:006.054031720.0
12612016-06-05 11:03:002016-06-05 14:57:000 days 03:54:0040.40 days 19:55:006.5371125.0
12942016-07-21 22:28:002016-07-21 23:21:000 days 00:53:0037.25 days 00:03:002.42662330.0
12982016-07-25 10:18:002016-07-25 15:48:000 days 05:30:0033.80 days 19:00:003.4003475760.0
13002016-07-27 15:25:002016-07-27 20:25:000 days 05:00:0012.21 days 01:04:004.6637878640.0
\n", + "
" + ], + "text/plain": [ + " start end duration rain_sum \\\n", + "105 2008-06-23 19:50:00 2008-06-23 23:23:00 0 days 03:33:00 34.9 \n", + "125 2008-07-17 14:14:00 2008-07-18 06:27:00 0 days 16:13:00 74.6 \n", + "127 2008-07-20 17:43:00 2008-07-21 03:55:00 0 days 10:12:00 27.7 \n", + "277 2009-07-18 08:55:00 2009-07-18 13:19:00 0 days 04:24:00 58.0 \n", + "286 2009-08-21 19:49:00 2009-08-21 20:29:00 0 days 00:40:00 34.2 \n", + "289 2009-08-28 23:41:00 2009-08-29 00:42:00 0 days 01:01:00 49.3 \n", + "291 2009-09-04 00:31:00 2009-09-04 18:18:00 0 days 17:47:00 69.0 \n", + "566 2011-08-03 19:52:00 2011-08-04 07:45:00 0 days 11:53:00 54.7 \n", + "684 2012-07-14 16:54:00 2012-07-15 13:07:00 0 days 20:13:00 59.8 \n", + "797 2013-05-05 20:46:00 2013-05-07 12:41:00 1 days 15:55:00 119.6 \n", + "844 2013-08-27 17:09:00 2013-08-27 23:48:00 0 days 06:39:00 43.3 \n", + "845 2013-08-28 11:41:00 2013-08-28 16:22:00 0 days 04:41:00 28.5 \n", + "947 2014-05-11 10:20:00 2014-05-11 23:18:00 0 days 12:58:00 55.7 \n", + "948 2014-05-12 17:22:00 2014-05-13 17:27:00 1 days 00:05:00 20.6 \n", + "1137 2015-07-08 11:50:00 2015-07-09 00:26:00 0 days 12:36:00 77.8 \n", + "1261 2016-06-05 11:03:00 2016-06-05 14:57:00 0 days 03:54:00 40.4 \n", + "1294 2016-07-21 22:28:00 2016-07-21 23:21:00 0 days 00:53:00 37.2 \n", + "1298 2016-07-25 10:18:00 2016-07-25 15:48:00 0 days 05:30:00 33.8 \n", + "1300 2016-07-27 15:25:00 2016-07-27 20:25:00 0 days 05:00:00 12.2 \n", + "\n", + " last_event max_return_period max_return_period_duration \n", + "105 2 days 21:52:00 5.363788 15.0 \n", + "125 0 days 04:09:00 6.457449 120.0 \n", + "127 2 days 02:48:00 2.831013 5760.0 \n", + "277 2 days 01:56:00 3.422038 240.0 \n", + "286 4 days 03:04:00 3.930161 20.0 \n", + "289 1 days 03:44:00 20.299093 20.0 \n", + "291 5 days 11:06:00 2.605659 1080.0 \n", + "566 1 days 23:09:00 12.962866 20.0 \n", + "684 1 days 01:15:00 3.720531 8640.0 \n", + "797 0 days 17:29:00 30.584981 2880.0 \n", + "844 0 days 13:56:00 2.452099 5.0 \n", + "845 0 days 11:53:00 3.543837 2880.0 \n", + "947 0 days 13:15:00 2.082195 2880.0 \n", + "948 0 days 18:04:00 2.798822 4320.0 \n", + "1137 8 days 13:52:00 6.054031 720.0 \n", + "1261 0 days 19:55:00 6.537112 5.0 \n", + "1294 5 days 00:03:00 2.426623 30.0 \n", + "1298 0 days 19:00:00 3.400347 5760.0 \n", + "1300 1 days 01:04:00 4.663787 8640.0 " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "events" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "start 2008-07-17 14:14:00\n", + "end 2008-07-18 06:27:00\n", + "duration 0 days 16:13:00\n", + "rain_sum 74.6\n", + "last_event 0 days 04:09:00\n", + "max_return_period 6.457449\n", + "max_return_period_duration 120.0\n", + "Name: 125, dtype: object" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# lets pick one event\n", + "event = events.loc[125]\n", + "event\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# you can also reduce the displayed duration steps\n", + "fig, caption = idf.event_plot(event, durations=idf.duration_steps[:11])\n", + "fig.tight_layout()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/genindex.html b/genindex.html new file mode 100644 index 0000000..e799c41 --- /dev/null +++ b/genindex.html @@ -0,0 +1,404 @@ + + + + + + + Index — Intensity Duration Frequency Analyse 0.2.3 documentation + + + + + + + + + + + +
+
+
+
+ + +

Index

+ +
+ _ + | A + | C + | D + | E + | F + | G + | H + | I + | M + | P + | R + | S + | T + | W + +
+

_

+ + + +
+ +

A

+ + + +
+ +

C

+ + +
+ +

D

+ + + +
+ +

E

+ + + +
+ +

F

+ + + +
+ +

G

+ + + +
+ +

H

+ + + +
+ +

I

+ + + +
    +
  • + idf_analysis.event_series_analysis + +
    • +
  • + idf_analysis.in_out + +
    • +
  • + idf_analysis.little_helpers + +
    • +
  • + idf_analysis.parameter_formulations + +
    • +
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+ + + + \ No newline at end of file diff --git a/index.html b/index.html new file mode 100644 index 0000000..1b144c3 --- /dev/null +++ b/index.html @@ -0,0 +1,147 @@ + + + + + + + + Welcome to Intensity Duration Frequency Analyse’s documentation! — Intensity Duration Frequency Analyse 0.2.3 documentation + + + + + + + + + + + + +
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+

Welcome to Intensity Duration Frequency Analyse’s documentation!

+
+

Contents:

+ +
+
+
+

Indices and tables

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+ + + + \ No newline at end of file diff --git a/objects.inv b/objects.inv new file mode 100644 index 0000000000000000000000000000000000000000..65820bfa46b6d43493b64fcfbce07289256a35c7 GIT binary patch literal 2575 zcmV+q3h?zKAX9K?X>NERX>N99Zgg*Qc_4OWa&u{KZXhxWBOp+6Z)#;@bUGkOZggdC zb7^#WAVhU?VRUJ4ZXiZ-WpQ<7Zew{ML2hAed2?k7BOq2~a&u{KZaN?^E;0%uAXI2& zAaZ4GVQFq;WpW^IW*~HEX>%ZEX>4U6X>%ZBZ*6dLWpi_7WFU2OX>MmAdTeQ8E(&0qqQg5WqzAp1PE%@&=k847kjL~?X^2#8p z1!2cB@mF#nEXH+>&v7LZp`j_S9JmgH-PblK$edu>)Fg?kk~^J3LQ`&SB_Cs4RdtDX zM_wb_S9~h#uk=U=b5q&Yw#_}SCk0K}`L?OCDDti*e>bGqonN=YM6H;(O8o&h+9%164!Xu^(KUV=P{ST)yc1}0&V;|4P>Kj^>LRXqGcFSvRMn+Fg9pb+66g4_ z?U00|=ulayyrZG!0_kfwI>q0}rR9WGGpcgo^vu);V1)^(*fB2mLq!jvqtA#bBUS4) z-m`IH>>Nl%yf5pxY*^K>AlkZcp(a&XvjG0;Ms!|);cXV;rHf%vdMpsUx!(+-NNp!i zp(9*daL=M@JT@(M2X%PgTvSHFE3xjKL>aPquw3fa)H0k$`gMB zj07SuZFlfI2u{L|xUGh;gKekcY1(!-G2`>H1D`yE!w)rL%y|jCVu?A>B|ZgaajH`m zI8f|Vgsr!|4v&gki0H_9*{CbNNplhZc&v_ODPtLlk0h()%WyGAhv5-z#_-ELE&NRy zhn1qkkPXZe`AE{kk-e0ofF~Mf%O`Q51`5kwc2)wQu{0;`i-~t7uQr8f8F7_06cpQs z{$=5XPablyP$`6+E2SB3voEuRBymKH$| z^%wMXiW`<@?&b^#V&(0wuFE$3C67Bk3wAMNgAWI#pLZtI&nVS7nJ-q+;zFPI{Ba6g!oz`{ZJqVHy*+4r!%000cHZ-fBzzOF0`EQF6t zn>Wy|G3fwB19e2ep~0*K77o@IPtad3XbXf3;HK%u9k6Gn3gDn1`0JaB(ByS=ARKVo z>PBZ!52%XZAp!iNZOH<=Lc<0@0U$iuBnGBDLI=VDpsj6P1C!RUK~R7Qho3&pXaUB9 za)-9%AHoxw0ywA;{As`n0U8m_Ap$sMa&Q9J854XkBv|s@fsSzGJA5!C0P^AHjS#a% z&^e+!^a9LN!ukS0Fbp^M1)1@}&T~gcfkF_zpabCm(GDBSK*ljaVwmyaDQNy9cqkw! z<{_K;7#|D?jQkVCZu5%ZAwm429*qO{g$fP>1IIbY++D*4K>;EB6!jjq$_J_K$^l1U z`U)}t1_0H`aZ6yr6MQfvF!BM%x~T#W&+)P-80e_L3I8@6yZ) z%iC_s0aqUSWqk-9I-+d>S02-^13++@ zqPi4v(ESolat<286S(sH)_GZr9So1S_2GpU9H6it)oEHfdXr-LBv6FjvUKUT# zY=o_CS1GQBor95cC)F1<%o%k{XyNix?Z0xYzi#oHMxoru; zat7PI=n9szw>2#p&El*RTej4A1Jlk(BgCSFjcKDxov!AMU{*=ln>o2J>Syi*&Pv+G zeC2AYtH}z)qJ)iUJejnYj9m>IZYeM1+Zd%^!F9DvM_D6eZKG$ap4}NSidb1^x-jf) zrU}eK)@*oDH4Nb*L8i+AM(f5U?nOex6QV!+Gx`QS;=Qu8I5;fJ&li2@bJi`&bMK(; zqX3I4FQOm6e2G4bU(cSC$JV7({tz)-&S__9K{jc1F19-GRrz*{=B~?#-d-}I&*D<_ zvZ|Xt2PrwI8DH#PG~uQ<0VyURZi0nrM}ohdFGsl3A|a2f++i%J`Gtoh(XT=(`XFx> z-3uuzD+W(-G}MYADTZ1xS2G9S3E#-vrR5+Ni@ zIZ{~ldbt`&!Jcy0;3r$|N_X{zWV~M`5i3`r|3IUp+%-85CR&N_6dl}aNxCDn9belM zRdi0RN%X!;Y6s&kNyR>Ob|O}yRWFAk&CXdvd~yB#Cl(=cvdKra=)>LX+xK_lsy@t* z2Fs%1`}jYKXdsDr=5K#~{Pf}VPYU*pSm8d@WmC+nQDxo?KUENQyk=#Y%|lHj)v;J4 zsIUpCM58EnlL%9V)TC~e2MucerE6H4*sc1cqV)}U9!CoFgQcM?65En=5uq28gYLUD z(|JiT9x@}taY_^7MO32R-@c3bla5p5={(5<+Db{hhjzYmfl3R?czm$ndrj(IdC;L2 z+hR&lTj3GU`&v>~<7+xy-~gH(ukXzxYKru6-Z(|(ck%Q2XZ}+KX(&EidEs65XYp4p zb>h8TdAd^dq`;T%|FbTymFMjPst=MMgz6!+uSV(97gcPq&6;(hn1=D&abos7$QnHUVI&PZl-n%;LG l=<7^Uau + + + + + + Python Module Index — Intensity Duration Frequency Analyse 0.2.3 documentation + + + + + + + + + + + + + + +
+
+
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+ + +

Python Module Index

+ +
+ i +
+ + + + + + + + + + + + + + + + + + + + + + + + + +
 
+ i
+ idf_analysis +
    + idf_analysis.event_series_analysis +
    + idf_analysis.in_out +
    + idf_analysis.little_helpers +
    + idf_analysis.parameter_formulations +
    + idf_analysis.plot_helpers +
    + idf_analysis.sww_utils +
+ + +
+
+
+
+ +
+
+ + + + \ No newline at end of file diff --git a/search.html b/search.html new file mode 100644 index 0000000..29922a7 --- /dev/null +++ b/search.html @@ -0,0 +1,101 @@ + + + + + + + Search — Intensity Duration Frequency Analyse 0.2.3 documentation + + + + + + + + + + + + + + + + + +
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