Streamflow Analysis and Visualizations
1. Load the Streamflow Data
[1]:
# Import necessary modules and the postprocessing library
import pandas as pd
import glob
from natsort import natsorted
import postprocessinglib
from postprocessinglib.evaluation import data, metrics, visuals
from postprocessinglib.utilities import _helper_functions as hlp
[2]:
# Define the input path and organize them
folder = r'C:\Users\udenzeU\OneDrive - EC-EC\Fuad_Mesh_Dataset\CanRCM_runs' ## new line
start_dates = [pd.to_datetime('1990-01-01'), pd.to_datetime('2026-01-01'), pd.to_datetime('2071-01-01')]
end_dates = [pd.to_datetime('2010-12-31'), pd.to_datetime('2055-12-31'), pd.to_datetime('2100-12-31')]
# Extract list of CSV files
csv_files = glob.glob(f"{folder}/**/MESH_output_streamflow.csv")
csv_files = natsorted(csv_files)
[3]:
# Load the Streamflow data using the `generate_dataframes` function
DATAFRAMES = data.generate_dataframes(csv_fpaths=csv_files)
for key, value in DATAFRAMES.items():
print(f"{key}")
for key, value in DATAFRAMES.items():
print(f"{key}:\n{value.head()}")
The start date for the Data is 1990-10-01
DF_1
DF_2
DF_3
DF_4
DF_5
DF_6
DF_OBSERVED
DF_SIMULATED_1
DF_SIMULATED_2
DF_SIMULATED_3
DF_SIMULATED_4
DF_SIMULATED_5
DF_SIMULATED_6
DF_MERGED
DF_1:
QOMEAS1 QOSIM1 QOMEAS2 QOSIM2 QOMEAS3 QOSIM3 QOMEAS4 \
1990-10-01 10.6 18.81220 NaN 2.017267 0.593 0.471522 7.70
1990-10-02 10.8 18.51336 NaN 1.686926 0.777 0.466355 8.11
1990-10-03 11.1 18.35229 NaN 1.599513 0.697 0.426448 7.74
1990-10-04 11.0 18.30125 NaN 1.680425 0.668 0.385862 7.43
1990-10-05 26.8 18.06379 NaN 1.764025 0.627 0.335419 8.07
QOSIM4 QOMEAS5 QOSIM5 ... QOMEAS50 QOSIM50 QOMEAS51 \
1990-10-01 39.71715 2.89 5.233493 ... NaN 0.067004 0.129
1990-10-02 36.69350 2.93 4.991893 ... NaN 0.073589 0.150
1990-10-03 34.88419 2.70 4.767472 ... NaN 0.079277 0.197
1990-10-04 34.04501 2.84 4.706922 ... NaN 0.083935 0.209
1990-10-05 33.97240 2.74 4.726519 ... NaN 0.087538 0.187
QOSIM51 QOMEAS52 QOSIM52 QOMEAS53 QOSIM53 QOMEAS54 \
1990-10-01 0.124661 194.0 571.2068 0.625 1.776447 378.0
1990-10-02 0.134962 315.0 559.9794 0.617 1.739489 376.0
1990-10-03 0.135775 480.0 548.8182 0.720 1.697753 405.0
1990-10-04 0.130387 507.0 538.4902 0.784 1.662737 338.0
1990-10-05 0.122777 296.0 529.0356 0.939 1.653770 377.0
QOSIM54
1990-10-01 650.3138
1990-10-02 645.3386
1990-10-03 637.9775
1990-10-04 627.9831
1990-10-05 616.5801
[5 rows x 108 columns]
DF_2:
QOMEAS1 QOSIM1 QOMEAS2 QOSIM2 QOMEAS3 QOSIM3 QOMEAS4 \
1990-10-01 10.6 18.82299 NaN 2.037463 0.593 0.473107 7.70
1990-10-02 10.8 18.72746 NaN 1.712869 0.777 0.466547 8.11
1990-10-03 11.1 18.70900 NaN 1.635449 0.697 0.426629 7.74
1990-10-04 11.0 18.75058 NaN 1.692260 0.668 0.370011 7.43
1990-10-05 26.8 18.57560 NaN 1.744896 0.627 0.311643 8.07
QOSIM4 QOMEAS5 QOSIM5 ... QOMEAS50 QOSIM50 QOMEAS51 \
1990-10-01 39.72040 2.89 5.233804 ... NaN 0.067004 0.129
1990-10-02 36.75991 2.93 5.050089 ... NaN 0.073589 0.150
1990-10-03 35.36140 2.70 4.939692 ... NaN 0.079277 0.197
1990-10-04 34.72194 2.84 4.886122 ... NaN 0.083935 0.209
1990-10-05 34.55230 2.74 4.888906 ... NaN 0.087537 0.187
QOSIM51 QOMEAS52 QOSIM52 QOMEAS53 QOSIM53 QOMEAS54 \
1990-10-01 0.124679 194.0 571.2067 0.625 1.776432 378.0
1990-10-02 0.135083 315.0 559.9792 0.617 1.739616 376.0
1990-10-03 0.135960 480.0 548.8182 0.720 1.699472 405.0
1990-10-04 0.130373 507.0 538.4933 0.784 1.669632 338.0
1990-10-05 0.122167 296.0 529.0383 0.939 1.654492 377.0
QOSIM54
1990-10-01 650.3159
1990-10-02 645.3452
1990-10-03 637.9928
1990-10-04 628.0175
1990-10-05 616.5956
[5 rows x 108 columns]
DF_3:
QOMEAS1 QOSIM1 QOMEAS2 QOSIM2 QOMEAS3 QOSIM3 QOMEAS4 \
1990-10-01 10.6 18.81733 NaN 2.017075 0.593 0.472017 7.70
1990-10-02 10.8 18.51571 NaN 1.687293 0.777 0.466216 8.11
1990-10-03 11.1 18.28233 NaN 1.608052 0.697 0.426281 7.74
1990-10-04 11.0 18.00542 NaN 1.596945 0.668 0.368857 7.43
1990-10-05 26.8 17.74772 NaN 1.563908 0.627 0.310941 8.07
QOSIM4 QOMEAS5 QOSIM5 ... QOMEAS50 QOSIM50 QOMEAS51 \
1990-10-01 39.71816 2.89 5.233574 ... NaN 0.067008 0.129
1990-10-02 36.69939 2.93 5.012283 ... NaN 0.073631 0.150
1990-10-03 34.91850 2.70 4.804056 ... NaN 0.079346 0.197
1990-10-04 33.98899 2.84 4.692329 ... NaN 0.084029 0.209
1990-10-05 33.39975 2.74 4.636430 ... NaN 0.087816 0.187
QOSIM51 QOMEAS52 QOSIM52 QOMEAS53 QOSIM53 QOMEAS54 \
1990-10-01 0.124698 194.0 571.2081 0.625 1.783594 378.0
1990-10-02 0.135202 315.0 560.0228 0.617 1.755848 376.0
1990-10-03 0.136357 480.0 548.9266 0.720 1.730276 405.0
1990-10-04 0.131554 507.0 538.6159 0.784 1.701723 338.0
1990-10-05 0.128352 296.0 529.2382 0.939 1.679545 377.0
QOSIM54
1990-10-01 650.3136
1990-10-02 645.3777
1990-10-03 638.1917
1990-10-04 628.3281
1990-10-05 617.1144
[5 rows x 108 columns]
DF_4:
QOMEAS1 QOSIM1 QOMEAS2 QOSIM2 QOMEAS3 QOSIM3 QOMEAS4 \
1990-10-01 10.6 18.81468 NaN 2.016999 0.593 0.471154 7.70
1990-10-02 10.8 18.63782 NaN 1.686672 0.777 0.465543 8.11
1990-10-03 11.1 18.49831 NaN 1.615934 0.697 0.425907 7.74
1990-10-04 11.0 18.10607 NaN 1.610370 0.668 0.368496 7.43
1990-10-05 26.8 17.75610 NaN 1.555644 0.627 0.310707 8.07
QOSIM4 QOMEAS5 QOSIM5 ... QOMEAS50 QOSIM50 QOMEAS51 \
1990-10-01 39.71692 2.89 5.233405 ... NaN 0.067004 0.129
1990-10-02 36.70995 2.93 5.025618 ... NaN 0.073589 0.150
1990-10-03 35.12986 2.70 4.876895 ... NaN 0.079277 0.197
1990-10-04 34.38066 2.84 4.786331 ... NaN 0.083935 0.209
1990-10-05 33.69102 2.74 4.697785 ... NaN 0.087538 0.187
QOSIM51 QOMEAS52 QOSIM52 QOMEAS53 QOSIM53 QOMEAS54 \
1990-10-01 0.124657 194.0 571.2067 0.625 1.776434 378.0
1990-10-02 0.134931 315.0 559.9793 0.617 1.743793 376.0
1990-10-03 0.135700 480.0 548.8315 0.720 1.725781 405.0
1990-10-04 0.130806 507.0 538.5229 0.784 1.692214 338.0
1990-10-05 0.128556 296.0 529.0711 0.939 1.664655 377.0
QOSIM54
1990-10-01 650.3141
1990-10-02 645.3383
1990-10-03 637.9814
1990-10-04 628.0611
1990-10-05 616.9236
[5 rows x 108 columns]
DF_5:
QOMEAS1 QOSIM1 QOMEAS2 QOSIM2 QOMEAS3 QOSIM3 QOMEAS4 \
1990-10-01 10.6 18.81892 NaN 2.016948 0.593 0.470999 7.70
1990-10-02 10.8 18.67601 NaN 1.685841 0.777 0.465530 8.11
1990-10-03 11.1 18.67621 NaN 1.614798 0.697 0.426422 7.74
1990-10-04 11.0 18.62771 NaN 1.644630 0.668 0.369590 7.43
1990-10-05 26.8 18.33330 NaN 1.655177 0.627 0.311000 8.07
QOSIM4 QOMEAS5 QOSIM5 ... QOMEAS50 QOSIM50 QOMEAS51 \
1990-10-01 39.71742 2.89 5.233759 ... NaN 0.067004 0.129
1990-10-02 36.73229 2.93 5.055761 ... NaN 0.073589 0.150
1990-10-03 35.30479 2.70 4.963171 ... NaN 0.079277 0.197
1990-10-04 34.72790 2.84 4.928212 ... NaN 0.083935 0.209
1990-10-05 34.43529 2.74 4.857594 ... NaN 0.087537 0.187
QOSIM51 QOMEAS52 QOSIM52 QOMEAS53 QOSIM53 QOMEAS54 \
1990-10-01 0.124656 194.0 571.2067 0.625 1.776467 378.0
1990-10-02 0.134999 315.0 559.9792 0.617 1.742075 376.0
1990-10-03 0.135976 480.0 548.8187 0.720 1.705304 405.0
1990-10-04 0.130532 507.0 538.4920 0.784 1.672976 338.0
1990-10-05 0.122235 296.0 529.0406 0.939 1.658495 377.0
QOSIM54
1990-10-01 650.3135
1990-10-02 645.3356
1990-10-03 637.9764
1990-10-04 627.9795
1990-10-05 616.5399
[5 rows x 108 columns]
DF_6:
QOMEAS1 QOSIM1 QOMEAS2 QOSIM2 QOMEAS3 QOSIM3 QOMEAS4 \
1990-10-01 10.6 18.80015 NaN 2.014894 0.593 0.472394 7.70
1990-10-02 10.8 18.15699 NaN 1.671266 0.777 0.470193 8.11
1990-10-03 11.1 17.73862 NaN 1.550409 0.697 0.429808 7.74
1990-10-04 11.0 17.51504 NaN 1.514462 0.668 0.372380 7.43
1990-10-05 26.8 17.36007 NaN 1.485547 0.627 0.315028 8.07
QOSIM4 QOMEAS5 QOSIM5 ... QOMEAS50 QOSIM50 QOMEAS51 \
1990-10-01 39.84330 2.89 5.310815 ... NaN 0.067004 0.129
1990-10-02 38.64192 2.93 5.281748 ... NaN 0.073589 0.150
1990-10-03 36.23276 2.70 4.800338 ... NaN 0.079279 0.197
1990-10-04 34.00919 2.84 4.585928 ... NaN 0.083955 0.209
1990-10-05 33.02460 2.74 4.522779 ... NaN 0.087610 0.187
QOSIM51 QOMEAS52 QOSIM52 QOMEAS53 QOSIM53 QOMEAS54 \
1990-10-01 0.124770 194.0 571.2163 0.625 1.780560 378.0
1990-10-02 0.137052 315.0 560.0678 0.617 1.783687 376.0
1990-10-03 0.143743 480.0 548.9528 0.720 1.769032 405.0
1990-10-04 0.139210 507.0 538.7165 0.784 1.740182 338.0
1990-10-05 0.130328 296.0 529.3161 0.939 1.718301 377.0
QOSIM54
1990-10-01 650.3185
1990-10-02 645.5105
1990-10-03 638.4096
1990-10-04 628.3748
1990-10-05 616.9622
[5 rows x 108 columns]
DF_OBSERVED:
QOMEAS1 QOMEAS2 QOMEAS3 QOMEAS4 QOMEAS5 QOMEAS6 QOMEAS7 \
1990-10-01 10.6 NaN 0.593 7.70 2.89 16.5 1.040
1990-10-02 10.8 NaN 0.777 8.11 2.93 17.0 0.893
1990-10-03 11.1 NaN 0.697 7.74 2.70 17.9 1.050
1990-10-04 11.0 NaN 0.668 7.43 2.84 18.7 1.260
1990-10-05 26.8 NaN 0.627 8.07 2.74 18.4 1.220
QOMEAS8 QOMEAS9 QOMEAS10 ... QOMEAS45 QOMEAS46 QOMEAS47 \
1990-10-01 33.8 7.47 26.0 ... 0.062 180.0 0.347
1990-10-02 35.9 6.95 25.4 ... 0.078 173.0 0.394
1990-10-03 35.3 6.41 24.3 ... 0.097 160.0 0.433
1990-10-04 36.0 7.22 24.9 ... 0.107 173.0 0.423
1990-10-05 38.5 8.34 31.2 ... 0.108 185.0 0.429
QOMEAS48 QOMEAS49 QOMEAS50 QOMEAS51 QOMEAS52 QOMEAS53 \
1990-10-01 121.0 NaN NaN 0.129 194.0 0.625
1990-10-02 124.0 NaN NaN 0.150 315.0 0.617
1990-10-03 126.0 NaN NaN 0.197 480.0 0.720
1990-10-04 156.0 NaN NaN 0.209 507.0 0.784
1990-10-05 178.0 NaN NaN 0.187 296.0 0.939
QOMEAS54
1990-10-01 378.0
1990-10-02 376.0
1990-10-03 405.0
1990-10-04 338.0
1990-10-05 377.0
[5 rows x 54 columns]
DF_SIMULATED_1:
QOSIM1 QOSIM2 QOSIM3 QOSIM4 QOSIM5 QOSIM6 \
1990-10-01 18.81220 2.017267 0.471522 39.71715 5.233493 55.38697
1990-10-02 18.51336 1.686926 0.466355 36.69350 4.991893 56.73228
1990-10-03 18.35229 1.599513 0.426448 34.88419 4.767472 52.69921
1990-10-04 18.30125 1.680425 0.385862 34.04501 4.706922 47.23211
1990-10-05 18.06379 1.764025 0.335419 33.97240 4.726519 42.63579
QOSIM7 QOSIM8 QOSIM9 QOSIM10 ... QOSIM45 QOSIM46 \
1990-10-01 0.959127 327.4590 7.509872 33.72101 ... 0.109795 244.4074
1990-10-02 0.683608 294.7451 7.420730 33.26928 ... 0.111214 240.5432
1990-10-03 0.474698 238.7148 7.299713 32.84546 ... 0.115004 236.8396
1990-10-04 0.338065 205.9231 7.184215 32.41231 ... 0.119858 233.4484
1990-10-05 0.296360 189.5354 7.016091 31.95961 ... 0.124613 230.3327
QOSIM47 QOSIM48 QOSIM49 QOSIM50 QOSIM51 QOSIM52 \
1990-10-01 0.201374 279.8744 0.037366 0.067004 0.124661 571.2068
1990-10-02 0.218515 275.3411 0.040456 0.073589 0.134962 559.9794
1990-10-03 0.234997 271.6444 0.043375 0.079277 0.135775 548.8182
1990-10-04 0.246596 268.7088 0.045532 0.083935 0.130387 538.4902
1990-10-05 0.251531 266.7316 0.046657 0.087538 0.122777 529.0356
QOSIM53 QOSIM54
1990-10-01 1.776447 650.3138
1990-10-02 1.739489 645.3386
1990-10-03 1.697753 637.9775
1990-10-04 1.662737 627.9831
1990-10-05 1.653770 616.5801
[5 rows x 54 columns]
DF_SIMULATED_2:
QOSIM1 QOSIM2 QOSIM3 QOSIM4 QOSIM5 QOSIM6 \
1990-10-01 18.82299 2.037463 0.473107 39.72040 5.233804 55.35415
1990-10-02 18.72746 1.712869 0.466547 36.75991 5.050089 56.63534
1990-10-03 18.70900 1.635449 0.426629 35.36140 4.939692 52.60967
1990-10-04 18.75058 1.692260 0.370011 34.72194 4.886122 47.01413
1990-10-05 18.57560 1.744896 0.311643 34.55230 4.888906 42.62983
QOSIM7 QOSIM8 QOSIM9 QOSIM10 ... QOSIM45 QOSIM46 \
1990-10-01 0.940115 327.3459 7.510547 33.72319 ... 0.109764 244.4068
1990-10-02 0.625418 294.2456 7.415940 33.28418 ... 0.111182 240.5426
1990-10-03 0.426822 237.6107 7.326240 33.02314 ... 0.114977 236.8397
1990-10-04 0.299344 205.2114 7.210837 32.65348 ... 0.120296 233.4486
1990-10-05 0.224173 189.4498 7.100391 32.26234 ... 0.124834 230.3194
QOSIM47 QOSIM48 QOSIM49 QOSIM50 QOSIM51 QOSIM52 \
1990-10-01 0.201399 279.8739 0.037366 0.067004 0.124679 571.2067
1990-10-02 0.218532 275.3401 0.040456 0.073589 0.135083 559.9792
1990-10-03 0.235004 271.6432 0.043376 0.079277 0.135960 548.8182
1990-10-04 0.246598 268.7141 0.045533 0.083935 0.130373 538.4933
1990-10-05 0.251551 266.7367 0.046629 0.087537 0.122167 529.0383
QOSIM53 QOSIM54
1990-10-01 1.776432 650.3159
1990-10-02 1.739616 645.3452
1990-10-03 1.699472 637.9928
1990-10-04 1.669632 628.0175
1990-10-05 1.654492 616.5956
[5 rows x 54 columns]
DF_SIMULATED_3:
QOSIM1 QOSIM2 QOSIM3 QOSIM4 QOSIM5 QOSIM6 \
1990-10-01 18.81733 2.017075 0.472017 39.71816 5.233574 55.36297
1990-10-02 18.51571 1.687293 0.466216 36.69939 5.012283 56.63144
1990-10-03 18.28233 1.608052 0.426281 34.91850 4.804056 52.59519
1990-10-04 18.00542 1.596945 0.368857 33.98899 4.692329 46.97252
1990-10-05 17.74772 1.563908 0.310941 33.39975 4.636430 42.45836
QOSIM7 QOSIM8 QOSIM9 QOSIM10 ... QOSIM45 QOSIM46 \
1990-10-01 0.945457 327.4098 7.507316 33.71287 ... 0.109772 244.4228
1990-10-02 0.630069 294.5028 7.396018 33.23731 ... 0.111257 240.5808
1990-10-03 0.427636 238.1237 7.263847 32.76100 ... 0.115550 236.8595
1990-10-04 0.297649 205.4872 7.098183 32.28337 ... 0.121884 233.4623
1990-10-05 0.213816 189.0710 6.976490 31.79988 ... 0.129358 230.3338
QOSIM47 QOSIM48 QOSIM49 QOSIM50 QOSIM51 QOSIM52 \
1990-10-01 0.201371 279.8741 0.037377 0.067008 0.124698 571.2081
1990-10-02 0.218496 275.3405 0.040615 0.073631 0.135202 560.0228
1990-10-03 0.234982 271.6434 0.043707 0.079346 0.136357 548.9266
1990-10-04 0.246566 268.7075 0.045964 0.084029 0.131554 538.6159
1990-10-05 0.251502 266.7234 0.047645 0.087816 0.128352 529.2382
QOSIM53 QOSIM54
1990-10-01 1.783594 650.3136
1990-10-02 1.755848 645.3777
1990-10-03 1.730276 638.1917
1990-10-04 1.701723 628.3281
1990-10-05 1.679545 617.1144
[5 rows x 54 columns]
DF_SIMULATED_4:
QOSIM1 QOSIM2 QOSIM3 QOSIM4 QOSIM5 QOSIM6 \
1990-10-01 18.81468 2.016999 0.471154 39.71692 5.233405 55.37167
1990-10-02 18.63782 1.686672 0.465543 36.70995 5.025618 56.65841
1990-10-03 18.49831 1.615934 0.425907 35.12986 4.876895 52.61231
1990-10-04 18.10607 1.610370 0.368496 34.38066 4.786331 46.98369
1990-10-05 17.75610 1.555644 0.310707 33.69102 4.697785 42.51191
QOSIM7 QOSIM8 QOSIM9 QOSIM10 ... QOSIM45 QOSIM46 \
1990-10-01 0.946689 327.3887 7.512612 33.72821 ... 0.109763 244.4068
1990-10-02 0.635171 294.4674 7.414495 33.26861 ... 0.111184 240.5426
1990-10-03 0.432410 237.9023 7.263924 32.78938 ... 0.114974 236.8395
1990-10-04 0.300691 205.3035 7.096633 32.29406 ... 0.119833 233.4585
1990-10-05 0.215567 189.4360 6.976479 31.82040 ... 0.124375 230.3404
QOSIM47 QOSIM48 QOSIM49 QOSIM50 QOSIM51 QOSIM52 \
1990-10-01 0.201382 279.8739 0.037366 0.067004 0.124657 571.2067
1990-10-02 0.218512 275.3398 0.040455 0.073589 0.134931 559.9793
1990-10-03 0.234991 271.6432 0.043375 0.079277 0.135700 548.8315
1990-10-04 0.246579 268.7086 0.045642 0.083935 0.130806 538.5229
1990-10-05 0.251522 266.7340 0.047070 0.087538 0.128556 529.0711
QOSIM53 QOSIM54
1990-10-01 1.776434 650.3141
1990-10-02 1.743793 645.3383
1990-10-03 1.725781 637.9814
1990-10-04 1.692214 628.0611
1990-10-05 1.664655 616.9236
[5 rows x 54 columns]
DF_SIMULATED_5:
QOSIM1 QOSIM2 QOSIM3 QOSIM4 QOSIM5 QOSIM6 \
1990-10-01 18.81892 2.016948 0.470999 39.71742 5.233759 55.36804
1990-10-02 18.67601 1.685841 0.465530 36.73229 5.055761 56.64683
1990-10-03 18.67621 1.614798 0.426422 35.30479 4.963171 52.60378
1990-10-04 18.62771 1.644630 0.369590 34.72790 4.928212 46.99369
1990-10-05 18.33330 1.655177 0.311000 34.43529 4.857594 42.58549
QOSIM7 QOSIM8 QOSIM9 QOSIM10 ... QOSIM45 QOSIM46 \
1990-10-01 0.946174 327.3978 7.511682 33.72020 ... 0.109763 244.4068
1990-10-02 0.631503 294.4772 7.439191 33.28514 ... 0.111199 240.5426
1990-10-03 0.428305 238.1075 7.360852 32.91175 ... 0.115041 236.8403
1990-10-04 0.301765 205.5259 7.192683 32.53777 ... 0.120048 233.4481
1990-10-05 0.230893 189.7418 7.067379 32.18650 ... 0.124871 230.3180
QOSIM47 QOSIM48 QOSIM49 QOSIM50 QOSIM51 QOSIM52 \
1990-10-01 0.201356 279.8741 0.037366 0.067004 0.124656 571.2067
1990-10-02 0.218454 275.3404 0.040455 0.073589 0.134999 559.9792
1990-10-03 0.234964 271.6435 0.043375 0.079277 0.135976 548.8187
1990-10-04 0.246555 268.7079 0.045532 0.083935 0.130532 538.4920
1990-10-05 0.251493 266.7243 0.046624 0.087537 0.122235 529.0406
QOSIM53 QOSIM54
1990-10-01 1.776467 650.3135
1990-10-02 1.742075 645.3356
1990-10-03 1.705304 637.9764
1990-10-04 1.672976 627.9795
1990-10-05 1.658495 616.5399
[5 rows x 54 columns]
DF_SIMULATED_6:
QOSIM1 QOSIM2 QOSIM3 QOSIM4 QOSIM5 QOSIM6 \
1990-10-01 18.80015 2.014894 0.472394 39.84330 5.310815 55.46951
1990-10-02 18.15699 1.671266 0.470193 38.64192 5.281748 57.11401
1990-10-03 17.73862 1.550409 0.429808 36.23276 4.800338 53.40980
1990-10-04 17.51504 1.514462 0.372380 34.00919 4.585928 48.29525
1990-10-05 17.36007 1.485547 0.315028 33.02460 4.522779 44.34890
QOSIM7 QOSIM8 QOSIM9 QOSIM10 ... QOSIM45 QOSIM46 \
1990-10-01 0.950678 327.4905 7.506587 33.71073 ... 0.110818 244.4111
1990-10-02 0.670923 294.9232 7.396421 33.22962 ... 0.112349 240.5636
1990-10-03 0.483199 238.9848 7.236720 32.75140 ... 0.118014 236.9252
1990-10-04 0.353471 206.5978 7.086346 32.25056 ... 0.124457 233.5274
1990-10-05 0.270863 190.0415 6.958402 31.75936 ... 0.132046 230.3860
QOSIM47 QOSIM48 QOSIM49 QOSIM50 QOSIM51 QOSIM52 \
1990-10-01 0.201404 279.9225 0.037372 0.067004 0.124770 571.2163
1990-10-02 0.218612 275.4638 0.041051 0.073589 0.137052 560.0678
1990-10-03 0.235321 271.7322 0.046757 0.079279 0.143743 548.9528
1990-10-04 0.247199 268.7787 0.051640 0.083955 0.139210 538.7165
1990-10-05 0.252455 266.7802 0.054384 0.087610 0.130328 529.3161
QOSIM53 QOSIM54
1990-10-01 1.780560 650.3185
1990-10-02 1.783687 645.5105
1990-10-03 1.769032 638.4096
1990-10-04 1.740182 628.3748
1990-10-05 1.718301 616.9622
[5 rows x 54 columns]
DF_MERGED:
Station1 \
QOMEAS QOSIM1 QOSIM2 QOSIM3 QOSIM4 QOSIM5
1990-10-01 10.6 18.81220 18.82299 18.81733 18.81468 18.81892
1990-10-02 10.8 18.51336 18.72746 18.51571 18.63782 18.67601
1990-10-03 11.1 18.35229 18.70900 18.28233 18.49831 18.67621
1990-10-04 11.0 18.30125 18.75058 18.00542 18.10607 18.62771
1990-10-05 26.8 18.06379 18.57560 17.74772 17.75610 18.33330
Station2 ... Station53 \
QOSIM6 QOMEAS QOSIM1 QOSIM2 ... QOSIM4 QOSIM5
1990-10-01 18.80015 NaN 2.017267 2.037463 ... 1.776434 1.776467
1990-10-02 18.15699 NaN 1.686926 1.712869 ... 1.743793 1.742075
1990-10-03 17.73862 NaN 1.599513 1.635449 ... 1.725781 1.705304
1990-10-04 17.51504 NaN 1.680425 1.692260 ... 1.692214 1.672976
1990-10-05 17.36007 NaN 1.764025 1.744896 ... 1.664655 1.658495
Station54 \
QOSIM6 QOMEAS QOSIM1 QOSIM2 QOSIM3 QOSIM4
1990-10-01 1.780560 378.0 650.3138 650.3159 650.3136 650.3141
1990-10-02 1.783687 376.0 645.3386 645.3452 645.3777 645.3383
1990-10-03 1.769032 405.0 637.9775 637.9928 638.1917 637.9814
1990-10-04 1.740182 338.0 627.9831 628.0175 628.3281 628.0611
1990-10-05 1.718301 377.0 616.5801 616.5956 617.1144 616.9236
QOSIM5 QOSIM6
1990-10-01 650.3135 650.3185
1990-10-02 645.3356 645.5105
1990-10-03 637.9764 638.4096
1990-10-04 627.9795 628.3748
1990-10-05 616.5399 616.9622
[5 rows x 378 columns]
2. Process and Aggregate the Data
[4]:
merged = DATAFRAMES["DF_MERGED"]
print(merged.head(10)) # Let's see what is looks like
# Use the long term seasonal aggregation to aggregate the data by time period into a single year time period
lt_mean_1990_2010 = data.long_term_seasonal(merged.loc[start_dates[0]: end_dates[0]], 'mean')
print(lt_mean_1990_2010)
lt_mean_2026_2055 = data.long_term_seasonal(merged.loc[start_dates[1]: end_dates[1]], 'mean')
print(lt_mean_2026_2055)
lt_mean_2071_2100 = data.long_term_seasonal(merged.loc[start_dates[2]: end_dates[2]], 'mean')
print(lt_mean_2071_2100)
Station1 \
QOMEAS QOSIM1 QOSIM2 QOSIM3 QOSIM4 QOSIM5
1990-10-01 10.6 18.81220 18.82299 18.81733 18.81468 18.81892
1990-10-02 10.8 18.51336 18.72746 18.51571 18.63782 18.67601
1990-10-03 11.1 18.35229 18.70900 18.28233 18.49831 18.67621
1990-10-04 11.0 18.30125 18.75058 18.00542 18.10607 18.62771
1990-10-05 26.8 18.06379 18.57560 17.74772 17.75610 18.33330
1990-10-06 32.3 17.88546 18.19083 17.55242 17.61585 18.02880
1990-10-07 22.2 17.70854 17.74622 17.48413 17.53787 17.93426
1990-10-08 19.1 17.61584 17.50106 17.49782 17.37719 17.75793
1990-10-09 18.0 17.60339 17.38908 17.56804 17.27410 17.53244
1990-10-10 18.7 17.58252 17.30749 17.56341 17.18378 17.33812
Station2 ... Station53 \
QOSIM6 QOMEAS QOSIM1 QOSIM2 ... QOSIM4 QOSIM5
1990-10-01 18.80015 NaN 2.017267 2.037463 ... 1.776434 1.776467
1990-10-02 18.15699 NaN 1.686926 1.712869 ... 1.743793 1.742075
1990-10-03 17.73862 NaN 1.599513 1.635449 ... 1.725781 1.705304
1990-10-04 17.51504 NaN 1.680425 1.692260 ... 1.692214 1.672976
1990-10-05 17.36007 NaN 1.764025 1.744896 ... 1.664655 1.658495
1990-10-06 17.36477 NaN 1.753367 1.696653 ... 1.644349 1.648170
1990-10-07 17.53780 NaN 1.696026 1.575941 ... 1.610938 1.661365
1990-10-08 17.84204 NaN 1.629444 1.442781 ... 1.573973 1.683672
1990-10-09 18.54160 NaN 1.570484 1.344068 ... 1.545863 1.653764
1990-10-10 19.75182 NaN 1.527051 1.286029 ... 1.522863 1.595742
Station54 \
QOSIM6 QOMEAS QOSIM1 QOSIM2 QOSIM3 QOSIM4
1990-10-01 1.780560 378.0 650.3138 650.3159 650.3136 650.3141
1990-10-02 1.783687 376.0 645.3386 645.3452 645.3777 645.3383
1990-10-03 1.769032 405.0 637.9775 637.9928 638.1917 637.9814
1990-10-04 1.740182 338.0 627.9831 628.0175 628.3281 628.0611
1990-10-05 1.718301 377.0 616.5801 616.5956 617.1144 616.9236
1990-10-06 1.697947 425.0 605.1698 605.1436 605.7986 605.6479
1990-10-07 1.672759 414.0 594.3897 594.4462 595.1940 594.9995
1990-10-08 1.641379 387.0 584.7931 584.6786 585.3295 585.1266
1990-10-09 1.606625 360.0 576.2973 575.7501 576.1378 576.0442
1990-10-10 1.571766 344.0 568.7361 567.7229 567.6948 567.6348
QOSIM5 QOSIM6
1990-10-01 650.3135 650.3185
1990-10-02 645.3356 645.5105
1990-10-03 637.9764 638.4096
1990-10-04 627.9795 628.3748
1990-10-05 616.5399 616.9622
1990-10-06 605.0979 605.5723
1990-10-07 594.4077 594.8701
1990-10-08 584.8270 585.0005
1990-10-09 576.0971 575.9014
1990-10-10 567.7687 567.5247
[10 rows x 378 columns]
Station1 \
QOMEAS QOSIM1 QOSIM2 QOSIM3 QOSIM4 QOSIM5 QOSIM6
jday
1 8.688947 6.881726 6.329358 6.872998 6.634378 7.059286 7.561311
2 8.713158 6.839291 6.272638 6.833150 6.582267 6.992485 7.505495
3 8.811053 6.795345 6.214155 6.802895 6.544520 6.929563 7.421361
4 8.915263 6.746180 6.157008 6.770981 6.517712 6.885207 7.348049
5 8.785789 6.695053 6.098863 6.734382 6.486347 6.837234 7.284106
... ... ... ... ... ... ... ...
362 8.790000 7.116120 6.559302 7.124756 7.184580 7.258777 7.755186
363 8.802000 7.059125 6.503955 7.060673 7.117174 7.194952 7.677179
364 8.780000 7.027963 6.445303 6.984221 7.050824 7.136133 7.600546
365 8.773500 7.008442 6.396284 6.902069 6.982612 7.079216 7.543631
366 8.192000 6.823170 6.074330 6.631625 7.066620 6.221796 6.614821
Station2 ... Station53 \
QOMEAS QOSIM1 QOSIM2 ... QOSIM4 QOSIM5 QOSIM6
jday ...
1 NaN 0.437714 0.446055 ... 4.249061 3.339744 4.321509
2 NaN 0.434168 0.442588 ... 4.220858 3.317945 4.292430
3 NaN 0.430864 0.439016 ... 4.191547 3.296917 4.263601
4 NaN 0.427870 0.435214 ... 4.162242 3.276467 4.234860
5 NaN 0.424847 0.431330 ... 4.133929 3.256299 4.206372
... ... ... ... ... ... ... ...
362 NaN 0.472667 0.455962 ... 4.506107 3.469082 4.391776
363 NaN 0.466327 0.452497 ... 4.475520 3.445635 4.363279
364 NaN 0.459700 0.448935 ... 4.443503 3.422384 4.334043
365 NaN 0.454329 0.445411 ... 4.411476 3.399591 4.303713
366 NaN 0.455541 0.398520 ... 3.975509 2.297205 4.060612
Station54 \
QOMEAS QOSIM1 QOSIM2 QOSIM3 QOSIM4 QOSIM5
jday
1 296.850000 175.342705 182.530490 206.114625 199.759155 199.623985
2 295.250000 173.499590 180.735115 204.059360 197.844780 197.497185
3 289.900000 171.702885 178.980170 202.024770 196.009280 195.444220
4 284.750000 169.953005 177.259015 200.013420 194.251845 193.425535
5 284.250000 168.250970 175.567095 198.033090 192.556435 191.410465
... ... ... ... ... ... ...
362 329.000000 188.654114 189.309995 219.752143 211.777200 208.548067
363 319.904762 186.312276 187.280976 217.407005 209.501719 206.224905
364 311.666667 184.026667 185.316871 215.129124 207.300529 203.903643
365 306.142857 181.812971 183.417838 212.912086 205.179229 201.623329
366 282.800000 180.348100 180.908380 190.001720 215.688460 157.955320
QOSIM6
jday
1 209.669020
2 207.524630
3 205.405745
4 203.328720
5 201.300710
... ...
362 218.024490
363 215.862714
364 213.701776
365 211.540271
366 184.355880
[366 rows x 378 columns]
Station1 \
QOMEAS QOSIM1 QOSIM2 QOSIM3 QOSIM4 QOSIM5 QOSIM6
jday
1 9.497436 8.012059 7.747979 7.485418 9.675025 6.775013 7.526834
2 9.467179 7.982701 7.514550 7.311571 9.526094 6.797863 7.466032
3 9.518974 7.882813 7.270108 7.233968 9.392362 6.837679 7.357944
4 9.554872 7.806743 7.181122 7.136918 9.357934 6.914636 7.296492
5 9.501026 7.701064 7.023647 7.013633 9.355133 6.729274 7.303007
... ... ... ... ... ... ... ...
362 10.680750 8.349402 8.291928 7.721718 9.709095 7.571163 7.829679
363 10.861500 8.340698 8.137079 7.646354 9.666007 7.462395 7.799689
364 10.468750 8.239368 8.844719 7.601181 9.757435 7.440780 7.769151
365 10.152250 8.107362 8.272678 7.704035 9.764286 7.310514 7.660968
366 10.871000 7.467140 11.106392 11.903857 7.910050 8.451425 7.516346
Station2 ... Station53 \
QOMEAS QOSIM1 QOSIM2 ... QOSIM4 QOSIM5 QOSIM6
jday ...
1 NaN 1.078234 0.802485 ... 5.443032 5.346525 6.038665
2 NaN 1.001594 0.690804 ... 5.460748 5.308397 5.997454
3 NaN 0.962342 0.628735 ... 5.464637 5.227135 5.955050
4 NaN 0.909247 0.586018 ... 5.477740 5.155999 5.917226
5 NaN 0.876816 0.553910 ... 5.500002 5.117408 5.891552
... ... ... ... ... ... ... ...
362 NaN 0.862982 0.637506 ... 5.662639 5.757467 6.244018
363 NaN 0.859687 0.867211 ... 5.603532 5.682644 6.194069
364 NaN 0.914297 1.076663 ... 5.543273 5.603902 6.134212
365 NaN 1.152977 0.946470 ... 5.501715 5.569907 6.076238
366 NaN 0.628353 1.008267 ... 7.164716 4.362534 7.657211
Station54 \
QOMEAS QOSIM1 QOSIM2 QOSIM3 QOSIM4 QOSIM5
jday
1 320.9268 223.353773 206.914820 225.687003 260.035790 249.358927
2 320.0000 220.819307 204.785427 223.850117 257.435590 246.571417
3 318.0488 218.564993 202.677017 221.968133 254.784823 243.532837
4 317.3903 216.621453 200.673497 219.907387 252.112643 240.344357
5 318.6829 214.914843 198.767113 217.724603 249.296050 237.142880
... ... ... ... ... ... ...
362 345.8095 239.057667 220.412230 240.663253 268.956783 264.090380
363 337.6429 236.735790 218.694063 238.101770 266.001730 262.455937
364 331.1429 234.093237 216.771613 235.523653 263.513943 261.422087
365 327.9048 231.300530 214.679140 233.029223 261.342067 259.424650
366 339.5454 210.742129 259.529600 244.107243 268.508543 266.790186
QOSIM6
jday
1 246.265580
2 243.727130
3 241.021000
4 238.208440
5 235.356377
... ...
362 257.563533
363 254.490903
364 251.743317
365 249.258110
366 282.972471
[366 rows x 378 columns]
Station1 \
QOMEAS QOSIM1 QOSIM2 QOSIM3 QOSIM4 QOSIM5
jday
1 9.497436 11.581862 10.354240 9.274994 12.612843 11.710418
2 9.467179 11.296332 10.332833 9.328039 12.760337 11.282132
3 9.518974 11.058306 10.452156 9.151275 12.247396 10.917497
4 9.554872 11.146149 10.342944 9.065909 11.804737 10.587664
5 9.501026 11.100729 10.447961 8.979059 11.923177 10.362997
... ... ... ... ... ... ...
362 10.680750 12.756324 11.683511 9.688269 12.870923 11.329784
363 10.861500 12.530487 11.239175 9.806731 12.567685 11.026859
364 10.468750 12.353217 10.676666 9.538857 12.259529 10.966772
365 10.152250 12.044333 10.442994 9.391621 12.291368 11.508266
366 10.871000 10.502868 11.166293 10.607102 19.220914 8.249683
Station2 ... Station53 \
QOSIM6 QOMEAS QOSIM1 QOSIM2 ... QOSIM4 QOSIM5
jday ...
1 12.580286 NaN 2.109496 1.650657 ... 5.171251 6.370264
2 12.317812 NaN 2.379630 1.402444 ... 5.298519 6.556137
3 12.450926 NaN 2.521775 1.254745 ... 5.334520 6.374160
4 12.352853 NaN 2.632218 1.118203 ... 5.290622 5.982963
5 12.479756 NaN 2.396125 1.207151 ... 5.207115 5.843653
... ... ... ... ... ... ... ...
362 13.588490 NaN 2.504868 1.716894 ... 5.412060 6.868195
363 13.188600 NaN 2.253684 1.641885 ... 5.362910 6.449978
364 13.066481 NaN 2.405706 1.906607 ... 5.300545 6.172657
365 13.124194 NaN 2.323351 1.691349 ... 5.261986 6.126747
366 13.495231 NaN 1.617362 0.928849 ... 5.399274 4.285660
Station54 \
QOSIM6 QOMEAS QOSIM1 QOSIM2 QOSIM3 QOSIM4
jday
1 6.640203 320.9268 291.001818 224.205940 262.398403 262.361857
2 6.564553 320.0000 292.135692 224.562897 263.481187 263.256003
3 6.762777 318.0488 293.633617 224.737863 264.509200 263.995110
4 6.810091 317.3903 296.609672 225.207030 264.629437 264.196570
5 6.508968 318.6829 300.188819 225.863497 264.230893 265.027927
... ... ... ... ... ... ...
362 6.714960 345.8095 295.782306 226.409131 264.477952 261.622024
363 6.664414 337.6429 294.528203 226.708645 262.678862 261.581600
364 6.767107 331.1429 293.616331 226.544472 262.305169 261.973217
365 6.753099 327.9048 293.691638 226.154910 263.557631 262.946793
366 5.363087 339.5454 321.142014 275.097886 271.656457 319.664186
QOSIM5 QOSIM6
jday
1 308.349823 277.467547
2 308.907077 278.432773
3 307.288937 280.192243
4 304.712293 281.534443
5 301.752657 280.661527
... ... ...
362 304.195210 278.438555
363 303.868783 279.571021
364 305.059431 280.273193
365 308.358569 280.723241
366 257.454143 280.624243
[366 rows x 378 columns]
[5]:
# Use the statistics aggregation to aggregate the multiple model simulations into maximum, median and minimum values per time period per station
min_1990_2010 = data.stat_aggregate(lt_mean_1990_2010, 'min')
max_1990_2010 = data.stat_aggregate(lt_mean_1990_2010, 'max')
median_1990_2010 = data.stat_aggregate(lt_mean_1990_2010, 'median')
# Let's see what this looks like
print(min_1990_2010)
print(max_1990_2010)
print(median_1990_2010)
# We do the same thing for the other time periods
min_2026_2055 = data.stat_aggregate(lt_mean_2026_2055, 'min')
max_2026_2055 = data.stat_aggregate(lt_mean_2026_2055, 'max')
median_2026_2055 = data.stat_aggregate(lt_mean_2026_2055, 'median')
min_2071_2100 = data.stat_aggregate(lt_mean_2071_2100, 'min')
max_2071_2100 = data.stat_aggregate(lt_mean_2071_2100, 'max')
median_2071_2100 = data.stat_aggregate(lt_mean_2071_2100, 'median')
Station1 Station2 Station3 Station4 Station5 Station6 Station7 \
MIN MIN MIN MIN MIN MIN MIN
jday
1 6.329358 0.433963 0.005967 13.416614 2.642453 13.954072 0.000863
2 6.272638 0.430830 0.005810 13.317578 2.630690 13.851633 0.000828
3 6.214155 0.427641 0.005658 13.215868 2.617990 13.745751 0.000795
4 6.157008 0.424554 0.005513 13.112881 2.605905 13.629465 0.000764
5 6.098863 0.421523 0.005376 13.033418 2.588094 13.516563 0.000932
... ... ... ... ... ... ... ...
362 6.559302 0.448137 0.006684 13.820552 2.752976 14.424254 0.001061
363 6.503955 0.444373 0.006527 13.670305 2.704638 14.293511 0.001014
364 6.445303 0.440619 0.006405 13.547787 2.660635 14.175765 0.000970
365 6.396284 0.437201 0.006232 13.533974 2.638948 14.054702 0.000928
366 6.074330 0.304425 0.002906 13.396320 2.408032 13.980550 0.001064
Station8 Station9 Station10 ... Station45 Station46 Station47 \
MIN MIN MIN ... MIN MIN MIN
jday ...
1 45.386764 1.761545 8.038454 ... 0.059385 52.017543 0.006906
2 44.966611 1.743846 7.963667 ... 0.058524 51.549738 0.006713
3 44.554778 1.726356 7.889466 ... 0.057686 51.089534 0.006527
4 44.157130 1.708925 7.811513 ... 0.056871 50.620569 0.006348
5 43.762791 1.691725 7.729536 ... 0.056073 50.140738 0.006175
... ... ... ... ... ... ... ...
362 46.864126 1.845808 8.460177 ... 0.073199 56.195055 0.007988
363 46.409457 1.827266 8.376001 ... 0.070764 55.533594 0.007756
364 45.964064 1.808843 8.286946 ... 0.068567 54.917650 0.007533
365 45.531159 1.790745 8.197750 ... 0.066596 54.347700 0.007318
366 41.736810 1.604902 7.067999 ... 0.019112 51.949692 0.006986
Station48 Station49 Station50 Station51 Station52 Station53 \
MIN MIN MIN MIN MIN MIN
jday
1 67.213441 0.002217 0.020372 0.056264 132.145547 3.339744
2 66.689587 0.002156 0.019971 0.055198 130.742038 3.317945
3 66.136216 0.002098 0.019586 0.054178 129.397567 3.296917
4 65.553270 0.002041 0.019215 0.053197 128.122824 3.276467
5 64.951402 0.001987 0.018857 0.052164 126.925241 3.256299
... ... ... ... ... ... ...
362 70.523642 0.002552 0.022074 0.070995 142.547857 3.469082
363 69.829993 0.002478 0.021612 0.069780 140.743329 3.445635
364 69.145647 0.002408 0.021167 0.068618 139.029500 3.422384
365 68.471519 0.002341 0.020738 0.067409 137.400445 3.399591
366 62.248892 0.002095 0.020133 0.037198 128.742292 2.297205
Station54
MIN
jday
1 175.342705
2 173.499590
3 171.702885
4 169.953005
5 168.250970
... ...
362 188.654114
363 186.312276
364 184.026667
365 181.812971
366 157.955320
[366 rows x 54 columns]
Station1 Station2 Station3 Station4 Station5 Station6 Station7 \
MAX MAX MAX MAX MAX MAX MAX
jday
1 7.561311 0.553525 0.012466 15.886335 3.237908 16.544171 0.003680
2 7.505495 0.542783 0.012212 15.813107 3.250960 16.390439 0.003289
3 7.421361 0.536111 0.011979 15.730800 3.225371 16.245192 0.002949
4 7.348049 0.531590 0.011768 15.595667 3.179306 16.112108 0.002649
5 7.284106 0.527665 0.011575 15.440840 3.133756 15.999205 0.002386
... ... ... ... ... ... ... ...
362 7.755186 0.573358 0.014246 16.567948 3.376463 17.415008 0.005572
363 7.677179 0.565944 0.013959 16.429694 3.361499 17.224286 0.005168
364 7.600546 0.562496 0.013667 16.267576 3.335828 17.021208 0.004699
365 7.543631 0.561053 0.013382 16.107201 3.311225 16.824943 0.004230
366 7.066620 0.574805 0.015039 15.572676 2.970901 16.814400 0.002430
Station8 Station9 Station10 ... Station45 Station46 Station47 \
MAX MAX MAX ... MAX MAX MAX
jday ...
1 50.728618 1.922126 8.710060 ... 0.145656 67.484654 0.011396
2 50.140278 1.900667 8.621738 ... 0.144483 66.768225 0.011021
3 49.587077 1.879568 8.529015 ... 0.143329 66.051493 0.010662
4 49.070209 1.858644 8.436946 ... 0.142190 65.335474 0.010318
5 48.569515 1.837774 8.340228 ... 0.141064 64.622213 0.009990
... ... ... ... ... ... ... ...
362 53.164467 2.036179 9.202895 ... 0.196208 73.307414 0.014610
363 52.687439 2.014023 9.105268 ... 0.193754 72.536870 0.014124
364 52.175534 1.992080 9.008135 ... 0.191339 71.775261 0.013666
365 51.621520 1.970208 8.918160 ... 0.189001 71.020025 0.013235
366 51.506564 1.929554 8.896427 ... 0.191024 70.807338 0.019224
Station48 Station49 Station50 Station51 Station52 Station53 \
MAX MAX MAX MAX MAX MAX
jday
1 76.812398 0.002703 0.026491 0.103033 159.694041 4.376752
2 76.122123 0.002630 0.026088 0.101636 158.027759 4.348566
3 75.427830 0.002559 0.025698 0.100328 156.407657 4.321025
4 74.891719 0.002492 0.025320 0.099063 154.832463 4.294704
5 74.410221 0.002427 0.024955 0.097823 153.299099 4.262943
... ... ... ... ... ... ...
362 80.772885 0.003102 0.029211 0.119557 171.380450 4.669909
363 79.980007 0.003015 0.028727 0.117818 169.514870 4.639386
364 79.221617 0.002931 0.028261 0.116122 167.675375 4.608742
365 78.489484 0.002851 0.027814 0.114451 165.862142 4.577824
366 75.905010 0.003079 0.035023 0.146949 161.648568 4.442289
Station54
MAX
jday
1 209.669020
2 207.524630
3 205.405745
4 203.328720
5 201.300710
... ...
362 219.752143
363 217.407005
364 215.129124
365 212.912086
366 215.688460
[366 rows x 54 columns]
Station1 Station2 Station3 Station4 Station5 Station6 Station7 \
MEDIAN MEDIAN MEDIAN MEDIAN MEDIAN MEDIAN MEDIAN
jday
1 6.877362 0.454934 0.006348 14.833756 3.037458 15.499540 0.001437
2 6.836220 0.451186 0.006195 14.713200 3.010386 15.346831 0.001397
3 6.799120 0.447405 0.006050 14.599002 2.983952 15.198291 0.001353
4 6.758580 0.443484 0.005916 14.483778 2.951622 15.056391 0.001291
5 6.714718 0.439555 0.005794 14.371473 2.921949 14.924626 0.001220
... ... ... ... ... ... ... ...
362 7.154668 0.480480 0.007606 15.720509 3.253017 16.426725 0.002347
363 7.088923 0.474488 0.007435 15.572571 3.212936 16.225201 0.002199
364 7.039394 0.468857 0.007279 15.419158 3.180026 16.048925 0.002065
365 6.995527 0.463908 0.007184 15.261940 3.120297 15.893501 0.001942
366 6.623223 0.476983 0.007478 13.874442 2.734390 14.302322 0.001537
Station8 Station9 Station10 ... Station45 Station46 Station47 \
MEDIAN MEDIAN MEDIAN ... MEDIAN MEDIAN MEDIAN
jday ...
1 48.939357 1.822414 8.410424 ... 0.115053 64.660850 0.009419
2 48.496819 1.801883 8.325576 ... 0.113778 63.925325 0.009111
3 48.039825 1.781508 8.240315 ... 0.112514 63.215957 0.008818
4 47.574933 1.761178 8.155541 ... 0.111261 62.535637 0.008539
5 47.114562 1.741513 8.072329 ... 0.110025 61.871840 0.008272
... ... ... ... ... ... ... ...
362 50.920417 1.904276 8.752683 ... 0.130895 67.901835 0.012363
363 50.435057 1.882610 8.663093 ... 0.129681 67.191319 0.011961
364 49.994150 1.862903 8.576311 ... 0.128493 66.471744 0.011568
365 49.579518 1.840325 8.491797 ... 0.127277 65.735810 0.011179
366 45.079465 1.796035 8.016777 ... 0.070047 56.589993 0.009844
Station48 Station49 Station50 Station51 Station52 Station53 \
MEDIAN MEDIAN MEDIAN MEDIAN MEDIAN MEDIAN
jday
1 74.501477 0.002462 0.023709 0.066366 154.606005 4.083224
2 73.636731 0.002393 0.023293 0.065324 153.033791 4.056474
3 72.779977 0.002326 0.022893 0.064346 151.461459 4.029263
4 71.944034 0.002262 0.022507 0.063421 149.884277 4.002116
5 71.136984 0.002201 0.022135 0.062513 148.303406 3.975511
... ... ... ... ... ... ...
362 78.020210 0.002781 0.025971 0.073762 162.136343 4.214647
363 77.233470 0.002695 0.025480 0.072278 160.366844 4.187268
364 76.418192 0.002614 0.025008 0.070904 158.666850 4.159637
365 75.577493 0.002536 0.024555 0.069630 157.013756 4.131608
366 67.310562 0.002677 0.025445 0.056939 135.750605 4.076035
Station54
MEDIAN
jday
1 199.691570
2 197.670982
3 195.726750
4 193.838690
5 191.983450
... ...
362 210.162633
363 207.863312
364 205.602086
365 203.401279
366 182.632130
[366 rows x 54 columns]
3. Visualize the Streamflow data
[6]:
# Extract the stations you want to plot
stations = ["Station6", "Station20", "Station27", "Station33", "Station46"]
# Because we have 3 time periods, let us write the lines and bounds into lists so its easier to follow
lines_to_plot = [median_1990_2010.loc[:, median_1990_2010.columns.map(lambda x: x[0] in stations and x[1] == "MEDIAN")],
median_2026_2055.loc[:, median_1990_2010.columns.map(lambda x: x[0] in stations and x[1] == "MEDIAN")],
median_2071_2100.loc[:, median_1990_2010.columns.map(lambda x: x[0] in stations and x[1] == "MEDIAN")]]
# and now for the bounds
#upper
ubounds_list = [max_1990_2010.loc[:, max_1990_2010.columns.map(lambda x: x[0] in stations and x[1] == "MAX")],
max_2026_2055.loc[:, max_1990_2010.columns.map(lambda x: x[0] in stations and x[1] == "MAX")],
max_2071_2100.loc[:, max_1990_2010.columns.map(lambda x: x[0] in stations and x[1] == "MAX")]]
#lower
lbounds_list = [min_1990_2010.loc[:, min_1990_2010.columns.map(lambda x: x[0] in stations and x[1] == "MIN")],
min_2026_2055.loc[:, min_1990_2010.columns.map(lambda x: x[0] in stations and x[1] == "MIN")],
min_2071_2100.loc[:, min_1990_2010.columns.map(lambda x: x[0] in stations and x[1] == "MIN")]]
[7]:
# Now that we have extracted out lines and bounds, let us plot the graps using the bounded plot function
visuals.bounded_plot(
lines = lines_to_plot,
upper_bounds = ubounds_list,
lower_bounds = lbounds_list,
grid=True,
)
# The function has a bunch of default colors, transparencies and legends, thus we can infact
# run test visuals without having to specify all the details
Number of linestyles provided is less than the minimum required. Number of Lines : 3. Number of linestyles provided is: 1. Defaulting to solid lines (-)
[27]:
# Plain, simple and not a lot of Customizations. Lets change that.
# Lets add color, titles, labels, legends and display a few metrics
visuals.bounded_plot(
lines = lines_to_plot,
upper_bounds = ubounds_list,
lower_bounds = lbounds_list,
linestyles=['(1.00, 0.70, 0.10)-', '(0.70, 0.00, 0.70)-', '(1.00, 0.00, 0.00)-'],
labels=['Julian Day', 'Streamflow'],
legend = ["median 1990 -> 2010", "median 2026 -> 2055", "median 2071 -> 2100"],
bound_legend=["Baseline 1990-2010", "2026-2055", "2071-2100"],
metrices= ["TTCOM", "TTP", "SPOD"],
grid=True,
transparency = [0.4],
title = 'Streamflow PLot'
)
