NEOPRENE notebooks¶

This notebook is part of a notebook collection available at the NEOPRENE Project Site for illustration, reproducibility and reusability purposes. This notebook is licensed under the the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

Calibrating and synthetic simulation of single-site Rainfall with the NSRPM.¶

In this script we calibrate the NSRPM (Neyman Scott Rectangular Pulse Model) using two rainfall series with different rainfall regimes: one with daily data located in the north of Spain, on a temperate climate (Cfb), and other with hourly data located in New Mexico (USA) on a semi-arid climate (BSh-BSk). The calibration parameters are used to simulate several decades of synthetic rainfall data which can be very useful for rainfall extreme analysis or disaggregation purposes, among other applications.

The script also contains:

a validation section where the observed series are compared with the simulated ones in terms of their statistics and exceedance probabilities.
a disaggregation function to disaggreate daily to hourly rainfall data.

Please, be advised that some of the processes may take up to 5 minutes in a modern computer.

Load packages¶

In [1]:

%load_ext autoreload
%autoreload 2

In [2]:

import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

In [3]:

from NEOPRENE.NSRP.HiperParams import Calibration as Calhps, Simulation as Simhps
from NEOPRENE.NSRP.Calibration import Calibration
from NEOPRENE.NSRP.Statistics import Statistics
from NEOPRENE.NSRP.Simulation import Simulation
from NEOPRENE.NSRP.Analysis import Analysis

In [4]:

import warnings
warnings.filterwarnings('ignore')

Example 1. Calibration and validation of daily data¶

Loading daily rainfall data for daily data point¶

In [5]:

Serie_PD = pd.read_csv('auxiliary-materials/Point_Daily.csv', sep=";", decimal=".", index_col=0, parse_dates=True)
Serie_PD[Serie_PD.values<0] = np.nan
Input_Serie = pd.DataFrame(index=Serie_PD.index)
Input_Serie['Rain'] = Serie_PD.values

Plotting daily data and seasonality¶

In [6]:

f, (ax0, ax1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]}, figsize=(20, 5))

t1=str(Input_Serie.index.year[0])
t2=str(Input_Serie.index.year[-1])
Input_Serie.plot(xlim=(t1, t2),  ylim=(0, 300), ax = ax0)
ax0.grid(True)
ax0.set_ylabel('mm/day')

grouped_m = Input_Serie.groupby(lambda x: x.month)
Month_sum = grouped_m.sum()*24/(len(Input_Serie>=0)/30)
Month_sum.plot(ax = ax1)
ax1.grid(True)
ax1.set_ylabel('mm/month');

Calibration¶

The calibration input file (Input_Cal_PD.yml) contains the hyperparameters to calibrate the model. This hyperparameters are loaded by the Calibration class within the NEOPRENE.NSRP.HiperParams module. In this notebook, the module is loaded as Calhps.

Input rainfall statistics can be directly calculated by the software from a time series or can be introduced from a file with the statistics (both options are shown in the present notebook).

A description of the hyperparamters available in the calibration file (Input_Cal_PD.yml) can be found within the doc folder of the project repository.

In [7]:

# Reading hiperparamteres for the calibration
hiper_params_cal = Calhps('./Input_Cal_PD.yml')

The original time series statistics are computed first, as the model calibrates against those statistics. Once the statistics have been computed, the library does not need the complete time series anymore.

In [8]:

# Input statistics calculated from a Rainfall serie
statistics_model_1 = Statistics(hiper_params_cal, time_series = Input_Serie)

# Input statistics from file 
#statistics_model_2 = Statistics(hiper_params_cal, file = 'auxiliary-materials/statististics_PD.csv')

The hyperparameters are passed to the Calibration class which return a calibrator object. This object can be used as a function, that receives the statistics that need to be reproduced by the model. The object can also save the calibrated parameters to disk for later use.

In [9]:

CAL              = Calibration(hiper_params_cal) 
CAL1             = CAL(statistics_model_1, verbose=False)
#CAL2             = CAL(statistics_model_2, verbose=False)

os.makedirs('./POINT_DAILY/CAL1/',exist_ok=True)

CAL1.save_files('./POINT_DAILY/CAL1/')
#CAL2.save_files('./CAL2/')


################################################################################
Adjustment of parameters using the Particle Swarm Optimization (PSO)

Simulation¶

The input simulation file (Input_Sim_PD.yml) contains the hypermarametes for rainfall simulation.

A description of the hyperparamters available in the simulation file (Input_Sim_PD.yml) can be found within the doc folder of the project repository.

The first thing we do is to load the hyperparameters

In [11]:

# Reading hiperparamteres for the simulation
hiper_params_sim = Simhps('./Input_Sim_PD.yml')

And those hyperparameters are then used to configure the Simulation class, which return a Simulation object that is used in a very similar way to the Calibration object.

In [12]:

SIM              = Simulation(hiper_params_sim) 

# Input parameters from the model
SIM1             = SIM(params_cal = CAL1)

# Input parameters from a dataframe

os.makedirs('./POINT_DAILY/SIM1/',exist_ok=True)
SIM1.save_files('./POINT_DAILY/SIM1/')

#SIM2             = SIM(params_cal = './CAL2/Calibrated_parameters.csv')
#SIM2.save_files('./SIM1/')


################################################################################
Synthetic simulation


Total cumulative rainfall - Analytical estimation =        99299.62
Total cumulative rainfall -             Simulated =        98859.84

Analysis of results¶

In this section, an analysis of the results is made.

In [13]:

Analysis_results = Analysis(CAL1,SIM1)

Verification¶

The curve of exceedence probabilities is shown to evaluate the quality of the fit of the simulations and the observations

In [14]:

# Comparing exceedence Probability betwen Observed and Simulated series
Analysis_results.exceedence_probability_fig(Input_Serie, SIM1.Daily_Simulation)

Validation¶

Another set of verification plots is generated where the value of different observed statistics (dashed line) is compared against the fitted statistics (blue squares), that is, the best values of the statistic that the optimal parameters were able to reproduce, and against the simulated statistics (red triangles), the values of the statistics that were obtained when simulating synthetic time series with the calibrated parameters.

The model correctly reproduces those parameters for which it is calibrated. Those parameters that do not participate in the calibration procedure may exhibit a more erratic behavior, thus the differences between the calibrated and simulated values of the statistics.

In [15]:

# Comparing the values of the statistcs (observed, fitted and simulated).
Analysis_results.compare_statistics_fig()

Example 2. Disaggregation of daily data to hourly data¶

Disaggregation process might take some minutes to dissagregate several decades of data. Here we select only 5 year as an example

In [16]:

# Period selected from the input (onserved) serie
year1 = 2000; year2 = 2005
x_series = Input_Serie[(Input_Serie.index.year >= year1) & (Input_Serie.index.year < year2)]

In [17]:

# Defining hourly synthetic serie
y_series = SIM1.Hourly_Simulation.copy()

In [18]:

# Daily-to-hourly disaggregation
Analysis_results.disaggregate_rainfall(x_series, y_series)
hourly_disaggregation = Analysis_results.hourly_disaggregation

In [19]:

# Resampling hourly disaggregated serie to daily one. 
#The series is resampled through groupby to avoid possible problems when dates are greater than 2262 and less than 1677.
daily_disaggregation = pd.DataFrame(hourly_disaggregation.groupby([(hourly_disaggregation.index.year),
                                                                   (hourly_disaggregation.index.month),
                                                                   (hourly_disaggregation.index.day)]).sum().values,
                                    index=pd.period_range(start=hourly_disaggregation.index[0],end=hourly_disaggregation.index[-1],freq='D'),columns=['Rain'])
# Calculating statistics from the disaggregated daily resample serie and for the period selected from the Input serie
statistics_model_3 = Statistics(hiper_params_cal, time_series = x_series)
statistics_disaggregated = Statistics(hiper_params_cal, time_series = daily_disaggregation)

In [20]:

# Comparing daily observed statiscis with dialy dissagregated ones

statistics_model_3.statististics_dataframe.compare(statistics_disaggregated.statististics_dataframe)

Out[20]:

	1		2		3		4		5		...	8		9		10		11		12
	self	other	self	other	self	other	self	other	self	other	...	self	other	self	other	self	other	self	other	self	other
mean	3.507258	2.799354	2.702817	2.698224	2.089677	2.081935	2.91	2.914867	2.270323	2.264621	...	1.465161	1.465842	1.595333	1.589176	3.713548	3.711987	4.966187	4.588271	5.054032	3.948681
var_1	41.278254	34.736253	26.169006	26.079205	24.566345	24.369237	31.787167	31.814725	25.8257	25.663644	...	14.645625	14.628236	24.285778	24.268923	48.780268	48.568544	64.706555	61.040213	101.595064	76.751037
var_2	119.565434	93.178984	66.536588	66.467611	63.601981	63.25332	82.352267	82.305725	77.780251	76.915392	...	40.120764	40.176382	49.032313	48.879519	116.462913	116.088647	180.721227	117.238622	300.214935	236.360579
var_3	215.945334	194.339293	131.377066	129.905686	94.772822	93.796437	114.2329	114.195465	148.364508	146.917671	...	69.314508	69.073412	78.142004	77.057658	192.214822	190.975687	216.536007	204.436874	496.819303	430.490138
var_4	298.109802	285.731876	145.907932	145.965362	153.684076	152.620861	196.617458	196.364875	251.422288	248.342357	...	95.344339	94.893352	83.931309	82.569719	266.877291	265.260172	331.735478	219.456351	896.459417	550.912086
autocorr_1_1	0.39951	0.431729	0.405202	0.402361	0.259598	0.259651	0.245841	0.244219	0.431689	0.42731	...	0.250708	0.249333	-0.001251	-0.008748	0.207701	0.20849	0.172506	0.193309	0.508178	0.498675
autocorr_2_1	0.19412	0.238424	0.173018	0.177214	0.092012	0.09022	0.046585	0.046296	0.216869	0.215802	...	0.234899	0.230817	-0.036542	-0.036248	0.042307	0.040583	-0.099715	-0.069953	0.320199	0.351899
autocorr_3_1	0.140389	0.190398	0.029453	0.030262	-0.007084	-0.006561	-0.010978	-0.009902	-0.036449	-0.037439	...	0.174173	0.171109	-0.03413	-0.032763	-0.026483	-0.027486	-0.096403	-0.066312	0.207887	0.266495
fih_1	0.516129	0.612903	0.521127	0.507042	0.6	0.593548	0.433333	0.42	0.587097	0.593548	...	0.63871	0.619355	0.673333	0.653333	0.496774	0.503226	0.460432	0.5	0.443548	0.567742
fiWW_1	0.75	0.75	0.632353	0.714286	0.629032	0.68254	0.776471	0.793103	0.609375	0.587302	...	0.696429	0.762712	0.55102	0.634615	0.705128	0.714286	0.68	0.693333	0.884058	0.850746
fiDD_1	0.765625	0.842105	0.662162	0.722222	0.752688	0.782609	0.707692	0.714286	0.725275	0.717391	...	0.828283	0.854167	0.782178	0.806122	0.701299	0.717949	0.625	0.693333	0.854545	0.886364
M3_1	632.177191	561.087011	331.922783	329.59892	417.915617	412.105625	552.230324	553.721389	409.973966	403.512372	...	242.032888	241.174446	648.894994	647.7617	841.705477	833.999763	1014.796293	970.169039	3242.221812	2215.17669

12 rows × 24 columns

In [21]:

Analysis_results.disaggregation_fig(hourly_disaggregation,daily_disaggregation,x_series,year1,year1)

Save Figures¶

To save the figures in png format we execute the following line

In [22]:

Analysis_results.save_figures('./POINT_DAILY/Figures/')

Example 3. Calibration and Simulation of hourly data¶

Loading hourly rainfall data for hourly data point¶

In [23]:

Serie_PH=pd.read_csv('auxiliary-materials/Point_Hourly.csv', sep=",", decimal=".", index_col=0, parse_dates = True)
Serie_PH[Serie_PH.values<0]=np.nan
Input_Serie=pd.DataFrame(index=Serie_PH.index)
Input_Serie['Rain']=Serie_PH.values

Plotting hourly data and seasonality¶

In [24]:

f, (ax0, ax1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]}, figsize=(20, 5))

t1=str(Input_Serie.index.year[0]); t2=str(Input_Serie.index.year[-1])
Input_Serie.plot(xlim=(t1, t2),  ylim=(0, 20), ax = ax0)
ax0.grid(True)
ax0.set_ylabel('mm/hour')

grouped_m = Input_Serie.groupby(lambda x: x.month)
Month_sum=grouped_m.sum()*24/(len(Input_Serie>=0)/30)
Month_sum.plot(ax = ax1)
ax1.grid(True)
ax1.set_ylabel('mm/month')

Out[24]:

Text(0, 0.5, 'mm/month')

Calibration¶

In [25]:

hiper_params_cal = Calhps('./Input_Cal_PH.yml')
statistics_model_1 = Statistics(hiper_params_cal, time_series = Input_Serie)

In [26]:

CAL              = Calibration(hiper_params_cal) 
CAL1             = CAL(statistics_model_1, verbose=False)

os.makedirs('./POINT_HOURLY/CAL1/', exist_ok=True)

CAL1.save_files('./POINT_HOURLY/CAL1/')


################################################################################
Adjustment of parameters using the Particle Swarm Optimization (PSO)

Simulation¶

In [27]:

hiper_params_sim = Simhps('./Input_Sim_PH.yml')

SIM              = Simulation(hiper_params_sim) 
SIM1             = SIM(params_cal = CAL1)
#SIM2             = SIM(params_cal = './CAL2/Calibrated_parameters.csv')


################################################################################
Synthetic simulation


Total cumulative rainfall - Analytical estimation - Storm 1 =         1116.16
Total cumulative rainfall - Analytical estimation - Storm 2 =          990.31
Total cumulative rainfall - Analytical estimation =         2106.47
Total cumulative rainfall -             Simulated =         1867.26

In [28]:

os.makedirs('./POINT_HOURLY/SIM1/',exist_ok=True)
SIM1.save_files('./POINT_HOURLY/SIM1/')

Analysis of results¶

In this section, an analysis of the results is made.

In [29]:

Analysis_results = Analysis(CAL1,SIM1)

Validation¶

In [30]:

# Comparing the values of the statistcs (observed, fitted and simulated).
Analysis_results.compare_statistics_fig()

In [31]:

# Comparing exceedence Probability betwen Observed and Simulated series
Analysis_results.exceedence_probability_fig(Input_Serie, SIM1.Hourly_Simulation)

Save Figures¶

To save the figures in png format we execute the following line

In [32]:

Analysis_results.save_figures('./POINT_HOURLY/Figures/')

Session info¶

In [33]:

!conda list

# packages in environment at C:\Anaconda3\envs\NEOPRENE_2024:
#
# Name                    Version                   Build  Channel
anyio                     4.2.0              pyhd8ed1ab_0    conda-forge
argon2-cffi               23.1.0             pyhd8ed1ab_0    conda-forge
argon2-cffi-bindings      21.2.0          py311ha68e1ae_4    conda-forge
arrow                     1.3.0              pyhd8ed1ab_0    conda-forge
asttokens                 2.4.1              pyhd8ed1ab_0    conda-forge
async-lru                 2.0.4              pyhd8ed1ab_0    conda-forge
attrs                     23.2.0             pyh71513ae_0    conda-forge
babel                     2.14.0             pyhd8ed1ab_0    conda-forge
beautifulsoup4            4.12.3             pyha770c72_0    conda-forge
bleach                    6.1.0              pyhd8ed1ab_0    conda-forge
brotli-python             1.1.0           py311h12c1d0e_1    conda-forge
bzip2                     1.0.8                hcfcfb64_5    conda-forge
ca-certificates           2023.11.17           h56e8100_0    conda-forge
cached-property           1.5.2                hd8ed1ab_1    conda-forge
cached_property           1.5.2              pyha770c72_1    conda-forge
certifi                   2023.11.17         pyhd8ed1ab_0    conda-forge
cffi                      1.16.0          py311ha68e1ae_0    conda-forge
charset-normalizer        3.3.2              pyhd8ed1ab_0    conda-forge
colorama                  0.4.6              pyhd8ed1ab_0    conda-forge
comm                      0.2.1              pyhd8ed1ab_0    conda-forge
contourpy                 1.2.0                    pypi_0    pypi
cycler                    0.12.1                   pypi_0    pypi
datetime                  5.4                      pypi_0    pypi
debugpy                   1.8.0           py311h12c1d0e_1    conda-forge
decorator                 5.1.1              pyhd8ed1ab_0    conda-forge
defusedxml                0.7.1              pyhd8ed1ab_0    conda-forge
entrypoints               0.4                pyhd8ed1ab_0    conda-forge
exceptiongroup            1.2.0              pyhd8ed1ab_2    conda-forge
executing                 2.0.1              pyhd8ed1ab_0    conda-forge
fonttools                 4.47.2                   pypi_0    pypi
fqdn                      1.5.1              pyhd8ed1ab_0    conda-forge
haversine                 2.8.1                    pypi_0    pypi
idna                      3.6                pyhd8ed1ab_0    conda-forge
importlib-metadata        7.0.1              pyha770c72_0    conda-forge
importlib_metadata        7.0.1                hd8ed1ab_0    conda-forge
importlib_resources       6.1.1              pyhd8ed1ab_0    conda-forge
ipykernel                 6.29.0             pyha63f2e9_0    conda-forge
ipython                   8.20.0             pyh7428d3b_0    conda-forge
isoduration               20.11.0            pyhd8ed1ab_0    conda-forge
jedi                      0.19.1             pyhd8ed1ab_0    conda-forge
jinja2                    3.1.3              pyhd8ed1ab_0    conda-forge
json5                     0.9.14             pyhd8ed1ab_0    conda-forge
jsonpointer               2.4             py311h1ea47a8_3    conda-forge
jsonschema                4.21.0             pyhd8ed1ab_0    conda-forge
jsonschema-specifications 2023.12.1          pyhd8ed1ab_0    conda-forge
jsonschema-with-format-nongpl 4.21.0             pyhd8ed1ab_0    conda-forge
jupyter-lsp               2.2.2              pyhd8ed1ab_0    conda-forge
jupyter_client            8.6.0              pyhd8ed1ab_0    conda-forge
jupyter_core              5.7.1           py311h1ea47a8_0    conda-forge
jupyter_events            0.9.0              pyhd8ed1ab_0    conda-forge
jupyter_server            2.12.5             pyhd8ed1ab_0    conda-forge
jupyter_server_terminals  0.5.1              pyhd8ed1ab_0    conda-forge
jupyterlab                4.0.10             pyhd8ed1ab_0    conda-forge
jupyterlab_pygments       0.3.0              pyhd8ed1ab_0    conda-forge
jupyterlab_server         2.25.2             pyhd8ed1ab_0    conda-forge
kiwisolver                1.4.5                    pypi_0    pypi
libffi                    3.4.2                h8ffe710_5    conda-forge
libsodium                 1.0.18               h8d14728_1    conda-forge
libsqlite                 3.44.2               hcfcfb64_0    conda-forge
libzlib                   1.2.13               hcfcfb64_5    conda-forge
markupsafe                2.1.3           py311ha68e1ae_1    conda-forge
matplotlib                3.8.2                    pypi_0    pypi
matplotlib-inline         0.1.6              pyhd8ed1ab_0    conda-forge
mistune                   3.0.2              pyhd8ed1ab_0    conda-forge
nbclient                  0.8.0              pyhd8ed1ab_0    conda-forge
nbconvert-core            7.14.2             pyhd8ed1ab_0    conda-forge
nbformat                  5.9.2              pyhd8ed1ab_0    conda-forge
neoprene                  1.0.1                     dev_0    <develop>
nest-asyncio              1.5.9              pyhd8ed1ab_0    conda-forge
notebook-shim             0.2.3              pyhd8ed1ab_0    conda-forge
numpy                     1.26.3                   pypi_0    pypi
openssl                   3.2.0                hcfcfb64_1    conda-forge
overrides                 7.4.0              pyhd8ed1ab_0    conda-forge
packaging                 23.2               pyhd8ed1ab_0    conda-forge
pandas                    2.1.4                    pypi_0    pypi
pandocfilters             1.5.0              pyhd8ed1ab_0    conda-forge
parso                     0.8.3              pyhd8ed1ab_0    conda-forge
pickleshare               0.7.5                   py_1003    conda-forge
pillow                    10.2.0                   pypi_0    pypi
pip                       23.3.2             pyhd8ed1ab_0    conda-forge
pkgutil-resolve-name      1.3.10             pyhd8ed1ab_1    conda-forge
platformdirs              4.1.0              pyhd8ed1ab_0    conda-forge
prometheus_client         0.19.0             pyhd8ed1ab_0    conda-forge
prompt-toolkit            3.0.42             pyha770c72_0    conda-forge
psutil                    5.9.7           py311ha68e1ae_0    conda-forge
pure_eval                 0.2.2              pyhd8ed1ab_0    conda-forge
pycparser                 2.21               pyhd8ed1ab_0    conda-forge
pygments                  2.17.2             pyhd8ed1ab_0    conda-forge
pyparsing                 3.1.1                    pypi_0    pypi
pyshp                     2.3.1                    pypi_0    pypi
pysocks                   1.7.1              pyh0701188_6    conda-forge
python                    3.11.0          hcf16a7b_0_cpython    conda-forge
python-dateutil           2.8.2              pyhd8ed1ab_0    conda-forge
python-fastjsonschema     2.19.1             pyhd8ed1ab_0    conda-forge
python-json-logger        2.0.7              pyhd8ed1ab_0    conda-forge
python_abi                3.11                    4_cp311    conda-forge
pytz                      2023.3.post1       pyhd8ed1ab_0    conda-forge
pywin32                   306             py311h12c1d0e_2    conda-forge
pywinpty                  2.0.12          py311h12c1d0e_0    conda-forge
pyyaml                    6.0.1           py311ha68e1ae_1    conda-forge
pyzmq                     25.1.2          py311h9250fbb_0    conda-forge
referencing               0.32.1             pyhd8ed1ab_0    conda-forge
requests                  2.31.0             pyhd8ed1ab_0    conda-forge
rfc3339-validator         0.1.4              pyhd8ed1ab_0    conda-forge
rfc3986-validator         0.1.1              pyh9f0ad1d_0    conda-forge
rpds-py                   0.17.1          py311hc37eb10_0    conda-forge
scipy                     1.11.4                   pypi_0    pypi
send2trash                1.8.2              pyh08f2357_0    conda-forge
setuptools                69.0.3             pyhd8ed1ab_0    conda-forge
shapely                   2.0.2                    pypi_0    pypi
six                       1.16.0             pyh6c4a22f_0    conda-forge
sniffio                   1.3.0              pyhd8ed1ab_0    conda-forge
soupsieve                 2.5                pyhd8ed1ab_1    conda-forge
stack_data                0.6.2              pyhd8ed1ab_0    conda-forge
terminado                 0.18.0             pyh5737063_0    conda-forge
tinycss2                  1.2.1              pyhd8ed1ab_0    conda-forge
tk                        8.6.13               h5226925_1    conda-forge
tomli                     2.0.1              pyhd8ed1ab_0    conda-forge
tornado                   6.3.3           py311ha68e1ae_1    conda-forge
tqdm                      4.66.1                   pypi_0    pypi
traitlets                 5.14.1             pyhd8ed1ab_0    conda-forge
types-python-dateutil     2.8.19.20240106    pyhd8ed1ab_0    conda-forge
typing-extensions         4.9.0                hd8ed1ab_0    conda-forge
typing_extensions         4.9.0              pyha770c72_0    conda-forge
typing_utils              0.1.0              pyhd8ed1ab_0    conda-forge
tzdata                    2023.4                   pypi_0    pypi
ucrt                      10.0.22621.0         h57928b3_0    conda-forge
uri-template              1.3.0              pyhd8ed1ab_0    conda-forge
urllib3                   2.1.0              pyhd8ed1ab_0    conda-forge
vc                        14.3                hcf57466_18    conda-forge
vc14_runtime              14.38.33130         h82b7239_18    conda-forge
vs2015_runtime            14.38.33130         hcb4865c_18    conda-forge
wcwidth                   0.2.13             pyhd8ed1ab_0    conda-forge
webcolors                 1.13               pyhd8ed1ab_0    conda-forge
webencodings              0.5.1              pyhd8ed1ab_2    conda-forge
websocket-client          1.7.0              pyhd8ed1ab_0    conda-forge
wheel                     0.42.0             pyhd8ed1ab_0    conda-forge
win_inet_pton             1.1.0              pyhd8ed1ab_6    conda-forge
winpty                    0.4.3                         4    conda-forge
xz                        5.2.6                h8d14728_0    conda-forge
yaml                      0.2.5                h8ffe710_2    conda-forge
zeromq                    4.3.5                h63175ca_0    conda-forge
zipp                      3.17.0             pyhd8ed1ab_0    conda-forge
zope-interface            6.1                      pypi_0    pypi