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phobson/conda_req.txt

Last active June 5, 2018 00:13
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The split-apply-combine strategy applied to linear regression
Raw
conda_req.txt
# This file may be used to create an environment using:
# $ conda create --name <env> --file <this file>
# platform: win-64
cycler=0.10.0=py35_0
decorator=4.0.9=py35_0
ipykernel=4.3.1=py35_0
ipython=4.1.2=py35_0
ipython_genutils=0.1.0=py35_0
ipywidgets=4.1.1=py35_0
jinja2=2.8=py35_0
jpeg=8d=vc14_0
jsonschema=2.4.0=py35_0
jupyter=1.0.0=py35_1
jupyter_client=4.1.1=py35_0
jupyter_console=4.1.0=py35_0
jupyter_core=4.0.6=py35_0
libpng=1.6.17=vc14_1
libtiff=4.0.6=vc14_1
markupsafe=0.23=py35_0
matplotlib=1.5.1=np110py35_0
mistune=0.7.1=py35_0
mkl=11.3.1=0
msvc_runtime=1.0.1=vc14_0
nbconvert=4.1.0=py35_0
nbformat=4.0.1=py35_0
nose=1.3.7=py35_0
notebook=4.1.0=py35_0
numpy=1.10.4=py35_0
pandas=0.17.1=np110py35_0
path.py=8.1.2=py35_1
patsy=0.4.0=np110py35_0
pickleshare=0.5=py35_0
pip=8.0.3=py35_0
pygments=2.1.1=py35_0
pyparsing=2.0.3=py35_0
pyqt=4.11.4=py35_5
pyreadline=2.1=py35_0
python=3.5.1=2
python-dateutil=2.4.2=py35_0
pytz=2015.7=py35_0
pyzmq=15.2.0=py35_0
qt=4.8.7=vc14_6
qtconsole=4.1.1=py35_0
scipy=0.17.0=np110py35_0
seaborn=0.7.0=py35_0
setuptools=20.1.1=py35_0
simplegeneric=0.8.1=py35_0
sip=4.16.9=py35_2
six=1.10.0=py35_0
statsmodels=0.6.1=np110py35_0
tk=8.5.18=vc14_0
tornado=4.3=py35_0
traitlets=4.1.0=py35_0
wheel=0.29.0=py35_0
zlib=1.2.8=vc14_2
Raw
fakedata.csv
conc date era location parameter days
123.52914203946618 2013-01-06 pre MW-1 TCE 0
119.73210230460235 2013-01-27 pre MW-1 TCE 21
126.0641465456202 2013-02-17 pre MW-1 TCE 42
121.76220112945919 2013-03-10 pre MW-1 TCE 63
125.53509980828159 2013-03-31 pre MW-1 TCE 84
117.18050337977171 2013-04-21 pre MW-1 TCE 105
116.61796215874692 2013-05-12 pre MW-1 TCE 126
144.36771295593925 2013-06-02 pre MW-1 TCE 147
114.72394061067713 2013-06-23 pre MW-1 TCE 168
114.96248415858344 2013-07-14 pre MW-1 TCE 189
119.68570549605978 2013-08-04 pre MW-1 TCE 210
114.00579774768289 2013-08-25 pre MW-1 TCE 231
114.84532926260337 2013-09-15 pre MW-1 TCE 252
111.71497466492465 2013-10-06 pre MW-1 TCE 273
112.61841679867288 2013-10-27 pre MW-1 TCE 294
110.32201454467196 2013-11-01 during MW-1 TCE 0
117.38638396586569 2013-11-04 during MW-1 TCE 3
99.99375653980398 2013-11-07 during MW-1 TCE 6
93.56167399155086 2013-11-10 during MW-1 TCE 9
90.93518049345657 2013-11-13 during MW-1 TCE 12
82.35548056772504 2013-11-16 during MW-1 TCE 15
76.02532903262016 2013-11-19 during MW-1 TCE 18
70.30623671971881 2013-11-22 during MW-1 TCE 21
68.21012593160663 2013-11-25 during MW-1 TCE 24
59.92200731876834 2013-11-28 during MW-1 TCE 27
54.25211305348309 2013-12-01 during MW-1 TCE 30
53.48476570046454 2013-12-04 during MW-1 TCE 33
41.83168688691159 2013-12-07 during MW-1 TCE 36
36.37571831374462 2013-12-10 during MW-1 TCE 39
31.01907997862268 2013-12-13 during MW-1 TCE 42
31.855101639323195 2014-01-01 post MW-1 TCE 0
31.206918208495612 2014-01-09 post MW-1 TCE 8
30.437589239720683 2014-01-17 post MW-1 TCE 16
30.45505534573859 2014-01-25 post MW-1 TCE 24
30.013194071942973 2014-02-02 post MW-1 TCE 32
28.884937325151594 2014-02-10 post MW-1 TCE 40
28.45390227278077 2014-02-18 post MW-1 TCE 48
29.82366445760144 2014-02-26 post MW-1 TCE 56
27.68423172367223 2014-03-06 post MW-1 TCE 64
27.173735962083434 2014-03-14 post MW-1 TCE 72
27.544441781603908 2014-03-22 post MW-1 TCE 80
27.425229622929308 2014-03-30 post MW-1 TCE 88
53.39966282386878 2014-04-07 post MW-1 TCE 96
26.0404059137335 2014-04-15 post MW-1 TCE 104
30.360599984430127 2014-04-23 post MW-1 TCE 112
254.19538628430846 2013-01-06 pre MW-1 DCE 0
246.80156981174437 2013-01-27 pre MW-1 DCE 21
247.19146930041813 2013-02-17 pre MW-1 DCE 42
239.66108138506846 2013-03-10 pre MW-1 DCE 63
235.77365576969004 2013-03-31 pre MW-1 DCE 84
235.66088092157042 2013-04-21 pre MW-1 DCE 105
228.75505291316918 2013-05-12 pre MW-1 DCE 126
227.8969778625727 2013-06-02 pre MW-1 DCE 147
222.12973532034013 2013-06-23 pre MW-1 DCE 168
230.98108878819173 2013-07-14 pre MW-1 DCE 189
220.55483902032776 2013-08-04 pre MW-1 DCE 210
211.6497313431593 2013-08-25 pre MW-1 DCE 231
207.8406751019786 2013-09-15 pre MW-1 DCE 252
203.93289115215626 2013-10-06 pre MW-1 DCE 273
200.95013301313168 2013-10-27 pre MW-1 DCE 294
201.16131807432328 2013-11-01 during MW-1 DCE 0
193.90226427657407 2013-11-04 during MW-1 DCE 3
187.71975846546655 2013-11-07 during MW-1 DCE 6
181.9693091301286 2013-11-10 during MW-1 DCE 9
173.98105815607758 2013-11-13 during MW-1 DCE 12
164.9200481236416 2013-11-16 during MW-1 DCE 15
157.4161423423674 2013-11-19 during MW-1 DCE 18
159.07628356780822 2013-11-22 during MW-1 DCE 21
143.55355650003153 2013-11-25 during MW-1 DCE 24
136.44380680523844 2013-11-28 during MW-1 DCE 27
129.4308579352776 2013-12-01 during MW-1 DCE 30
121.59323729047561 2013-12-04 during MW-1 DCE 33
115.19284610399369 2013-12-07 during MW-1 DCE 36
108.04808393510328 2013-12-10 during MW-1 DCE 39
103.58030457256527 2013-12-13 during MW-1 DCE 42
103.94745645630128 2014-01-01 post MW-1 DCE 0
100.94222533228707 2014-01-09 post MW-1 DCE 8
103.85610011001067 2014-01-17 post MW-1 DCE 16
100.37890073711647 2014-01-25 post MW-1 DCE 24
98.7595738367796 2014-02-02 post MW-1 DCE 32
98.44466186868992 2014-02-10 post MW-1 DCE 40
97.01340054490078 2014-02-18 post MW-1 DCE 48
96.00645887661945 2014-02-26 post MW-1 DCE 56
95.46550030079685 2014-03-06 post MW-1 DCE 64
93.97394709401362 2014-03-14 post MW-1 DCE 72
95.19808185882034 2014-03-22 post MW-1 DCE 80
92.6193014157367 2014-03-30 post MW-1 DCE 88
96.14965847766415 2014-04-07 post MW-1 DCE 96
91.92133258624467 2014-04-15 post MW-1 DCE 104
91.75394708807764 2014-04-23 post MW-1 DCE 112
33.05657794132975 2013-01-06 pre MW-1 PCE 0
34.04167325477549 2013-01-27 pre MW-1 PCE 21
30.945761673023867 2013-02-17 pre MW-1 PCE 42
32.344421041935604 2013-03-10 pre MW-1 PCE 63
36.38271167351001 2013-03-31 pre MW-1 PCE 84
31.983092746146017 2013-04-21 pre MW-1 PCE 105
34.818261670612905 2013-05-12 pre MW-1 PCE 126
32.598327131110445 2013-06-02 pre MW-1 PCE 147
33.316405257217504 2013-06-23 pre MW-1 PCE 168
34.94457426998054 2013-07-14 pre MW-1 PCE 189
33.7792878698855 2013-08-04 pre MW-1 PCE 210
34.409571229808414 2013-08-25 pre MW-1 PCE 231
34.547613963090086 2013-09-15 pre MW-1 PCE 252
47.580856465393154 2013-10-06 pre MW-1 PCE 273
37.02442294468243 2013-10-27 pre MW-1 PCE 294
38.35051091891114 2013-11-01 during MW-1 PCE 0
34.462427812677035 2013-11-04 during MW-1 PCE 3
32.48850932067632 2013-11-07 during MW-1 PCE 6
29.86143942985412 2013-11-10 during MW-1 PCE 9
29.00191234554431 2013-11-13 during MW-1 PCE 12
26.70786848625887 2013-11-16 during MW-1 PCE 15
29.10393592446233 2013-11-19 during MW-1 PCE 18
29.36459699583993 2013-11-22 during MW-1 PCE 21
31.025410559927504 2013-11-25 during MW-1 PCE 24
23.714547186778777 2013-11-28 during MW-1 PCE 27
17.922969422690542 2013-12-01 during MW-1 PCE 30
15.765803074570616 2013-12-04 during MW-1 PCE 33
17.870198751063747 2013-12-07 during MW-1 PCE 36
14.359469542553837 2013-12-10 during MW-1 PCE 39
10.180744099439718 2013-12-13 during MW-1 PCE 42
12.571147355035373 2014-01-01 post MW-1 PCE 0
15.635201956700826 2014-01-09 post MW-1 PCE 8
13.917602561416352 2014-01-17 post MW-1 PCE 16
14.522525961276642 2014-01-25 post MW-1 PCE 24
14.886473774542424 2014-02-02 post MW-1 PCE 32
15.627457575715235 2014-02-10 post MW-1 PCE 40
20.911929955149457 2014-02-18 post MW-1 PCE 48
18.62031169313942 2014-02-26 post MW-1 PCE 56
19.80284696815915 2014-03-06 post MW-1 PCE 64
34.02376500041348 2014-03-14 post MW-1 PCE 72
24.59347577099718 2014-03-22 post MW-1 PCE 80
24.406649619260783 2014-03-30 post MW-1 PCE 88
24.82624417824214 2014-04-07 post MW-1 PCE 96
34.951676996083805 2014-04-15 post MW-1 PCE 104
39.146774311787105 2014-04-23 post MW-1 PCE 112
105.26921496969135 2013-01-06 pre MW-2 TCE 0
101.27078821174815 2013-01-27 pre MW-2 TCE 21
95.83452712044419 2013-02-17 pre MW-2 TCE 42
95.495902011684 2013-03-10 pre MW-2 TCE 63
92.82982368718316 2013-03-31 pre MW-2 TCE 84
91.1398924072869 2013-04-21 pre MW-2 TCE 105
92.50201908699808 2013-05-12 pre MW-2 TCE 126
85.05368635218699 2013-06-02 pre MW-2 TCE 147
83.66639878033885 2013-06-23 pre MW-2 TCE 168
83.02459277791118 2013-07-14 pre MW-2 TCE 189
79.49126728783065 2013-08-04 pre MW-2 TCE 210
76.70908514889452 2013-08-25 pre MW-2 TCE 231
79.17088562094061 2013-09-15 pre MW-2 TCE 252
74.47337356239913 2013-10-06 pre MW-2 TCE 273
70.48973996468547 2013-10-27 pre MW-2 TCE 294
72.4437200908659 2013-11-01 during MW-2 TCE 0
71.74711796317351 2013-11-04 during MW-2 TCE 3
65.94537934065 2013-11-07 during MW-2 TCE 6
65.91342570780803 2013-11-10 during MW-2 TCE 9
60.38958896827559 2013-11-13 during MW-2 TCE 12
56.7202673046587 2013-11-16 during MW-2 TCE 15
53.24416411968205 2013-11-19 during MW-2 TCE 18
51.48080601059174 2013-11-22 during MW-2 TCE 21
49.24480858743331 2013-11-25 during MW-2 TCE 24
49.21301320809862 2013-11-28 during MW-2 TCE 27
42.8563441097427 2013-12-01 during MW-2 TCE 30
41.4919714250514 2013-12-04 during MW-2 TCE 33
36.20801643155208 2013-12-07 during MW-2 TCE 36
33.47698747743448 2013-12-10 during MW-2 TCE 39
31.436218152996663 2013-12-13 during MW-2 TCE 42
30.44118738698771 2014-01-01 post MW-2 TCE 0
34.525138778518894 2014-01-09 post MW-2 TCE 8
32.79862302069233 2014-01-17 post MW-2 TCE 16
32.122718141612864 2014-01-25 post MW-2 TCE 24
32.23270177487632 2014-02-02 post MW-2 TCE 32
50.263743270666225 2014-02-10 post MW-2 TCE 40
30.488949182467163 2014-02-18 post MW-2 TCE 48
29.605209507543258 2014-02-26 post MW-2 TCE 56
31.543720400928947 2014-03-06 post MW-2 TCE 64
30.90480511827157 2014-03-14 post MW-2 TCE 72
30.14909563019266 2014-03-22 post MW-2 TCE 80
29.699319211407825 2014-03-30 post MW-2 TCE 88
30.095050744065155 2014-04-07 post MW-2 TCE 96
31.139616933190187 2014-04-15 post MW-2 TCE 104
29.879395204145602 2014-04-23 post MW-2 TCE 112
333.6349969039696 2013-01-06 pre MW-2 DCE 0
298.353415173977 2013-01-27 pre MW-2 DCE 21
296.3260570483836 2013-02-17 pre MW-2 DCE 42
293.6657756425811 2013-03-10 pre MW-2 DCE 63
293.68629157370106 2013-03-31 pre MW-2 DCE 84
289.41830234117765 2013-04-21 pre MW-2 DCE 105
287.98288662353286 2013-05-12 pre MW-2 DCE 126
286.1681389388184 2013-06-02 pre MW-2 DCE 147
283.53612533517287 2013-06-23 pre MW-2 DCE 168
295.2061998318369 2013-07-14 pre MW-2 DCE 189
284.28584962494983 2013-08-04 pre MW-2 DCE 210
285.00417257707875 2013-08-25 pre MW-2 DCE 231
274.8328690831084 2013-09-15 pre MW-2 DCE 252
278.9009625167875 2013-10-06 pre MW-2 DCE 273
273.064857828803 2013-10-27 pre MW-2 DCE 294
274.3243309546276 2013-11-01 during MW-2 DCE 0
260.2560223968513 2013-11-04 during MW-2 DCE 3
239.10085871194988 2013-11-07 during MW-2 DCE 6
223.82720317484262 2013-11-10 during MW-2 DCE 9
211.9893000475949 2013-11-13 during MW-2 DCE 12
191.48820575844078 2013-11-16 during MW-2 DCE 15
186.64591223053304 2013-11-19 during MW-2 DCE 18
163.29173598377534 2013-11-22 during MW-2 DCE 21
145.8428842513852 2013-11-25 during MW-2 DCE 24
130.74339852771737 2013-11-28 during MW-2 DCE 27
113.31216049141679 2013-12-01 during MW-2 DCE 30
98.20742696454394 2013-12-04 during MW-2 DCE 33
87.9517086084528 2013-12-07 during MW-2 DCE 36
74.06650649620592 2013-12-10 during MW-2 DCE 39
97.95581497935035 2013-12-13 during MW-2 DCE 42
51.50319699064526 2014-01-01 post MW-2 DCE 0
51.009549723356585 2014-01-09 post MW-2 DCE 8
51.43836254736396 2014-01-17 post MW-2 DCE 16
51.318975663634575 2014-01-25 post MW-2 DCE 24
52.63868761904332 2014-02-02 post MW-2 DCE 32
54.088809282364885 2014-02-10 post MW-2 DCE 40
53.293925992869106 2014-02-18 post MW-2 DCE 48
58.49111005603868 2014-02-26 post MW-2 DCE 56
53.515980091520824 2014-03-06 post MW-2 DCE 64
53.63932064890161 2014-03-14 post MW-2 DCE 72
53.65439395497007 2014-03-22 post MW-2 DCE 80
53.357068273658506 2014-03-30 post MW-2 DCE 88
53.66087514164254 2014-04-07 post MW-2 DCE 96
54.091875304531015 2014-04-15 post MW-2 DCE 104
62.38785720844364 2014-04-23 post MW-2 DCE 112
65.04842009120831 2013-01-06 pre MW-2 PCE 0
68.83827299553742 2013-01-27 pre MW-2 PCE 21
59.59755443383201 2013-02-17 pre MW-2 PCE 42
59.069319901404185 2013-03-10 pre MW-2 PCE 63
59.293905060814296 2013-03-31 pre MW-2 PCE 84
60.268254042083385 2013-04-21 pre MW-2 PCE 105
60.93232103511965 2013-05-12 pre MW-2 PCE 126
63.61023476156474 2013-06-02 pre MW-2 PCE 147
59.49505368059587 2013-06-23 pre MW-2 PCE 168
71.34655781537072 2013-07-14 pre MW-2 PCE 189
57.676821838516794 2013-08-04 pre MW-2 PCE 210
56.59801014568396 2013-08-25 pre MW-2 PCE 231
55.915875024409985 2013-09-15 pre MW-2 PCE 252
55.89983439124815 2013-10-06 pre MW-2 PCE 273
57.23144074585753 2013-10-27 pre MW-2 PCE 294
55.54286189864905 2013-11-01 during MW-2 PCE 0
57.81710019926078 2013-11-04 during MW-2 PCE 3
59.63507063136525 2013-11-07 during MW-2 PCE 6
47.585265647477925 2013-11-10 during MW-2 PCE 9
49.85527321736887 2013-11-13 during MW-2 PCE 12
42.6003084767801 2013-11-16 during MW-2 PCE 15
42.72338017141761 2013-11-19 during MW-2 PCE 18
45.12237655736989 2013-11-22 during MW-2 PCE 21
36.78795075232477 2013-11-25 during MW-2 PCE 24
32.833000440391594 2013-11-28 during MW-2 PCE 27
30.793113448350372 2013-12-01 during MW-2 PCE 30
28.68879857092062 2013-12-04 during MW-2 PCE 33
26.31284169449277 2013-12-07 during MW-2 PCE 36
23.77938430314763 2013-12-10 during MW-2 PCE 39
21.43712778194844 2013-12-13 during MW-2 PCE 42
22.152973825611664 2014-01-01 post MW-2 PCE 0
20.721100760858352 2014-01-09 post MW-2 PCE 8
21.932608579900688 2014-01-17 post MW-2 PCE 16
32.070671689078225 2014-01-25 post MW-2 PCE 24
22.44795002358588 2014-02-02 post MW-2 PCE 32
22.732393372777373 2014-02-10 post MW-2 PCE 40
28.488361771106085 2014-02-18 post MW-2 PCE 48
22.567274041300344 2014-02-26 post MW-2 PCE 56
30.076192036410987 2014-03-06 post MW-2 PCE 64
23.962639305252196 2014-03-14 post MW-2 PCE 72
25.595094566411134 2014-03-22 post MW-2 PCE 80
25.788246342331707 2014-03-30 post MW-2 PCE 88
29.64100584729982 2014-04-07 post MW-2 PCE 96
25.82284785479302 2014-04-15 post MW-2 PCE 104
25.70642924749875 2014-04-23 post MW-2 PCE 112
Raw
reg_helpers.py
import numpy
import pandas
import statsmodels.api as sm
import seaborn
from matplotlib import pyplot
from statsmodels.sandbox.regression.predstd import wls_prediction_std
def generate_fake_data(dates, startconc, stopconc, **othercols):
N = len(dates)
noise = numpy.random.lognormal(0.5, 1.25, size=N)
conc = noise + numpy.linspace(startconc, stopconc, num=N)
data = pandas.DataFrame(dict(date=dates, conc=conc, **othercols))
data['days'] = (data['date'] - data['date'].min()).dt.days
return data
def plot_raw_data(data, **fgopts):
fg = (
seaborn.FacetGrid(aspect=2, data=data, **fgopts)
.map(pyplot.scatter, 'date', 'conc')
.set_xticklabels(rotation=30, rotation_mode='anchor', ha='right')
.add_legend()
)
return fg
def plot_modeled_data(modeled, **fgopts):
fg = (
seaborn.FacetGrid(aspect=2, data=modeled, **fgopts)
.map(pyplot.fill_between, 'date', 'ci_upper', 'ci_lower', zorder=0, alpha=0.2, label='95% CI')
.map(pyplot.plot, 'date', 'fit', label='best-fit')
.map(pyplot.scatter, 'date', 'conc', label='raw data')
.set_xticklabels(rotation=30, rotation_mode='anchor', ha='right')
)
return fg
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Raw
sac-regression.ipynb
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