incomplete display of the picture plotted by matplotlib - matplotlib

I used matplotlib to generate a keyboard. But after I wrote the code, I found that the picture displayed is incomplete. I have tried a few ways to solve the problem, like adjusting the dpi and changing the size of the picture. However, they are all useless.
this is the output
Here is the code:
ax=fig.add_subplot(111)
#ax.set_axis_off()
ax.plot(X,Y)
currentAxis = plt.gca()
generate_keyboard(currentAxis)
plt.text(0,0,"g")
plt.savefig(fname="3.jpg")
plt.show()`
def generate_keyboard(currentAxis):
rect1=patches.Rectangle((-0.5,-1),1,2,linewidth=2,edgecolor='r',facecolor='none')
rect2=patches.Rectangle((-1.5,-1),1,2,linewidth=2,edgecolor='r',facecolor='none')
rect3=patches.Rectangle((-2.5,-1),1,2,linewidth=2,edgecolor='r',facecolor='none')
rect4=patches.Rectangle((-3.5,-1),1,2,linewidth=2,edgecolor='r',facecolor='none')
rect5=patches.Rectangle((-4.5,-1),1,2,linewidth=2,edgecolor='r',facecolor='none')
rect6=patches.Rectangle((0.5,-1),1,2,linewidth=2,edgecolor='r',facecolor='none')
rect7=patches.Rectangle((1.5,-1),1,2,linewidth=2,edgecolor='r',facecolor='none')
rect8=patches.Rectangle((2.5,-1),1,2,linewidth=2,edgecolor='r',facecolor='none')
rect9=patches.Rectangle((3.5,-1),1,2,linewidth=2,edgecolor='r',facecolor='none')
rect10=patches.Rectangle((-1,1),1,2,linewidth=2,edgecolor='r',facecolor='none')
rect11=patches.Rectangle((-2,1),1,2,linewidth=2,edgecolor='r',facecolor='none')
rect12=patches.Rectangle((-3,1),1,2,linewidth=2,edgecolor='r',facecolor='none')
rect13=patches.Rectangle((-4,1),1,2,linewidth=2,edgecolor='r',facecolor='none')
rect14=patches.Rectangle((-5,1),1,2,linewidth=2,edgecolor='r',facecolor='none')
rect15=patches.Rectangle((0,1),1,2,linewidth=2,edgecolor='r',facecolor='none')
rect16=patches.Rectangle((1,1),1,2,linewidth=2,edgecolor='r',facecolor='none')
rect17=patches.Rectangle((2,1),1,2,linewidth=2,edgecolor='r',facecolor='none')
rect18=patches.Rectangle((3,1),1,2,linewidth=2,edgecolor='r',facecolor='none')
rect19=patches.Rectangle((4,1),1,2,linewidth=2,edgecolor='r',facecolor='none')
rect20=patches.Rectangle((-0.5,-3),1,2,linewidth=2,edgecolor='r',facecolor='none')
rect21=patches.Rectangle((-1.5,-3),1,2,linewidth=2,edgecolor='r',facecolor='none')
rect22=patches.Rectangle((-2.5,-3),1,2,linewidth=2,edgecolor='r',facecolor='none')
rect23=patches.Rectangle((-3.5,-3),1,2,linewidth=2,edgecolor='r',facecolor='none')
rect24=patches.Rectangle((0.5,-3),1,2,linewidth=2,edgecolor='r',facecolor='none')
rect25=patches.Rectangle((1.5,-3),1,2,linewidth=2,edgecolor='r',facecolor='none')
rect26=patches.Rectangle((2.5,-3),1,2,linewidth=2,edgecolor='r',facecolor='none')
currentAxis.add_patch(rect1)
currentAxis.add_patch(rect2)
currentAxis.add_patch(rect3)
currentAxis.add_patch(rect4)
currentAxis.add_patch(rect5)
currentAxis.add_patch(rect6)
currentAxis.add_patch(rect7)
currentAxis.add_patch(rect8)
currentAxis.add_patch(rect9)
currentAxis.add_patch(rect10)
currentAxis.add_patch(rect11)
currentAxis.add_patch(rect12)
currentAxis.add_patch(rect13)
currentAxis.add_patch(rect14)
currentAxis.add_patch(rect15)
currentAxis.add_patch(rect16)
currentAxis.add_patch(rect17)
currentAxis.add_patch(rect18)
currentAxis.add_patch(rect19)currentAxis.add_patch(rect20)
currentAxis.add_patch(rect21)
currentAxis.add_patch(rect22)
currentAxis.add_patch(rect23)
currentAxis.add_patch(rect24)
currentAxis.add_patch(rect25)
currentAxis.add_patch(rect26)

Related

Getting rid of a specific line in plots

x = [0.0000000,0.0082707,0.0132000, 0.0255597, 0.0503554, 0.0751941, 0.1000570,
0.1498328, 0.1996558, 0.2495240, 0.2994312, 0.3993490, 0.4993711, 0.5994664,
0.6996058, 0.7997553, 0.8998927, 0.9499514, 1.0000000, 0.0000000, 0.006114,
0.0062188, 0.0087532, 0.0138088, 0.0264052, 0.0515127, 0.0765762, 0.1016176,
0.1516652, 0.2016828, 0.2516733, 0.3016387, 0.4015163, 0.5013438, 0.6011363,
0.7008976, 0.8006328, 0.9003380, 0.9501740, 1.0000000]
y = [0.0000000, 0.0233088, 0.0298517, 0.0425630, 0.0603942, 0.0739301, 0.0850687,
0.1023515, 0.1149395, 0.1230325, 0.1272298, 0.1253360, 0.1130538, 0.0934796,
0.0695104, 0.0445423, 0.0207728, 0.0098870, 0.0000000, 0.0000000, -.0208973,
-.0210669, -.0247377, -.0307807, -.0416431, -.0548774, -.0637165, -.0703581,
-.0801452, -.0869356, -.0910290, -.0926252, -.0905235, -.0834273, -.0728351,
-.0591463, -.0428603, -.0235778, -.0122883, 0.0000000]
plt.plot(x,y)
This is a data point I have web scraped from a website. If I plot this there is a line that crosses from (0,0) to (1,0) which I don't want. I've tried manually removing points and seeing what points I need to remove but I can't seem to get it to work. There are two pairs of the same points in this data point. How can you remove the line that crosses from (0,0) to (1,0) and can you automate it by using if statement?

If you look carefully at your data, there appears to be a discontinuity. Your x values start going up, then goes back to 0, and goes up again. This discontinuity is creating the horizontal line that's bothering you. You can loop through x and y, detect when the values of x drop instead of increase, find this index and separate the two lists. However, using np.diff can help with this too. In this specific case, there's only one drop, so you have only one negative diff value. Here's my code and the graph I got. It might not generalize to any number of discontinuities, but it fixes your case.
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
x = [0.0000000,0.0082707,0.0132000, 0.0255597, 0.0503554, 0.0751941, 0.1000570,
0.1498328, 0.1996558, 0.2495240, 0.2994312, 0.3993490, 0.4993711, 0.5994664,
0.6996058, 0.7997553, 0.8998927, 0.9499514, 1.0000000, 0.0000000, 0.006114,
0.0062188, 0.0087532, 0.0138088, 0.0264052, 0.0515127, 0.0765762, 0.1016176,
0.1516652, 0.2016828, 0.2516733, 0.3016387, 0.4015163, 0.5013438, 0.6011363,
0.7008976, 0.8006328, 0.9003380, 0.9501740, 1.0000000]
y = [0.0000000, 0.0233088, 0.0298517, 0.0425630, 0.0603942, 0.0739301, 0.0850687,
0.1023515, 0.1149395, 0.1230325, 0.1272298, 0.1253360, 0.1130538, 0.0934796,
0.0695104, 0.0445423, 0.0207728, 0.0098870, 0.0000000, 0.0000000, -.0208973,
-.0210669, -.0247377, -.0307807, -.0416431, -.0548774, -.0637165, -.0703581,
-.0801452, -.0869356, -.0910290, -.0926252, -.0905235, -.0834273, -.0728351,
-.0591463, -.0428603, -.0235778, -.0122883, 0.0000000]
discont = np.diff(x).argmin()
x1 = x[:discont+1]
y1 = y[:discont+1]
x2 = x[discont+1:]
y2 = y[discont+1:]
plt.plot(x1, y1)
plt.plot(x2, y2)
plt.show()

Plotting Line and Stacked Bar plots on the same graph in Time Series

So, my goal is to display two line plots and one stacked bar plot on the same graph using pandas matplotlib in Time Series. I have followed the steps in the following link Plot Pandas DataFrame as Bar and Line on the same one chart, but it did not work as expected. What I get is only the stacked bar plot displayed:
Here is my code:
import pandas as pd
import matplotlib.pyplot as plt
df_max_scaled = pd.read_csv('dataset.csv',index_col='sample_date')
start, end = '2021-06-01', '2021-07-08'
fig, ax = plt.subplots(figsize=(20, 10))
ax.plot(df_max_scaled.loc[start:end,'cumPeopleVaccinatedFirstDoseByVaccinationDate'],marker='.', linestyle='-', linewidth=0.5, label='FirstVaccine')
ax.plot(df_max_scaled.loc[start:end,'cumPeopleVaccinatedSecondDoseByVaccinationDate'],marker='.', linestyle='-', linewidth=0.5, label='SecondVaccine')
df_max_scaled.loc[start:end].plot(kind='bar', stacked=True, y=['B.1.1.7','B.1.617.2'],ax=ax)
Here is the dataset.csv:
sample_date,B.1.1.7,B.1.617.2,cumPeopleVaccinatedFirstDoseByVaccinationDate,cumPeopleVaccinatedSecondDoseByVaccinationDate
2020-06-14,0.00031938677738741617,0.0,0.0,0.0
2020-06-22,0.0,0.0,0.0,0.0
2020-08-30,0.0,0.0,0.0,0.0
2020-09-03,0.0,0.0,0.0,0.0
2020-09-04,0.0,0.0,0.0,0.0
2020-09-20,0.00031938677738741617,0.0,0.0,0.0
2020-09-21,0.00031938677738741617,0.0,0.0,0.0
2020-09-23,0.00031938677738741617,0.0,0.0,0.0
2020-09-30,0.0006387735547748323,0.0,0.0,0.0
2020-10-01,0.00031938677738741617,0.0,0.0,0.0
2020-10-08,0.00031938677738741617,0.0,0.0,0.0
2020-10-09,0.0,0.0,0.0,0.0
2020-10-11,0.0009581603321622484,0.0,0.0,0.0
2020-10-12,0.0,0.0,0.0,0.0
2020-10-15,0.0009581603321622484,0.0,0.0,0.0
2020-10-17,0.00031938677738741617,0.0,0.0,0.0
2020-10-19,0.00031938677738741617,0.0,0.0,0.0
2020-10-20,0.0006387735547748323,0.0,0.0,0.0
2020-10-21,0.0047908016608112424,0.0,0.0,0.0
2020-10-22,0.00415202810603641,0.0,0.0,0.0
2020-10-23,0.0019163206643244969,0.0,0.0,0.0
2020-10-24,0.0015969338869370809,0.0,0.0,0.0
2020-10-25,0.00031938677738741617,0.0,0.0,0.0
2020-10-26,0.0006387735547748323,0.0,0.0,0.0
2020-10-27,0.00031938677738741617,0.0,0.0,0.0
2020-10-29,0.005748961992973491,0.0,0.0,0.0
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2020-11-01,0.012456084318109231,0.0,0.0,0.0
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2020-12-25,0.09070584477802619,0.0,0.021933593059878045,0.0
2020-12-26,0.2823379112104759,0.0,0.02197991135736565,0.0
2020-12-27,0.19801980198019803,0.0,0.02210968199312663,0.0
2020-12-28,0.1782178217821782,0.0,0.022348710875761225,0.0
2020-12-29,0.4231874800383264,0.0,0.023582259774451067,8.051846123113752e-05
2020-12-30,0.264771638454168,0.0,0.026482023405562088,0.00023117729180303356
2020-12-31,0.30150111785372086,0.0,0.02885891962703448,0.0004006508263802765
2021-01-01,0.2034493771957841,0.0,0.02951309279315082,0.00043944512225621693
2021-01-02,0.5397636537847333,0.0,0.030306909009292643,0.0006053533939731596
2021-01-03,0.41807729160012774,0.0,0.03073999832460101,0.0007397743936961635
2021-01-04,0.4573618652187799,0.0,0.03147405070318454,0.0010677079775792268
2021-01-05,0.39220696263174704,0.0,0.03301920263977241,0.002730497156138075
2021-01-06,0.25902267646119453,0.0,0.03538426784468655,0.005584401826727731
2021-01-07,0.3813478122005749,0.0,0.040748244304933734,0.008378297122441045
2021-01-08,0.49313318428617053,0.0,0.047347569460573134,0.01048031962546243
2021-01-09,0.5889492175023954,0.0,0.05298479740839862,0.012722975863488133
2021-01-10,0.4072181411689556,0.0,0.05586873341556816,0.014303340400672711
2021-01-11,0.5959757266049186,0.0,0.05884138881312539,0.01486601653921911
2021-01-12,0.7294793995528586,0.0,0.06347779745643731,0.01523750986933594
2021-01-13,0.46343021398914086,0.0,0.0701575311765152,0.015462545025122218
2021-01-14,0.5752155860747366,0.0,0.07776344557315519,0.015593859657204565
2021-01-15,0.6528265729798787,0.0,0.0865841433411029,0.015673283829835016
2021-01-16,0.4768444586394123,0.0,0.0942485511305701,0.015717161272759305
2021-01-17,0.48067709996806135,0.0,0.09819584937766074,0.015743847794763136
2021-01-18,0.6384541679974449,0.0,0.10217414118267024,0.015776394055725482
2021-01-19,0.5835196422868093,0.0,0.11000275395363061,0.01581991850232697
2021-01-20,0.5263494091344618,0.0,0.11841203936949415,0.01587629201507845
2021-01-21,0.5052698818268924,0.0,0.12788680443316394,0.015928253073794904
2021-01-22,0.6684765250718621,0.0,0.14033306032005413,0.015950385943234592
2021-01-23,0.510380070265091,0.0,0.15252191881090607,0.01596965954245958
2021-01-24,0.4065793676141808,0.0,0.157528741496126,0.015986356268528117
2021-01-25,0.9773235388054935,0.0,0.16253823740880094,0.01600291179606753
2021-01-26,0.8543596295113383,0.0,0.1685279343667078,0.01601685515081821
2021-01-27,0.8361545832002555,0.0,0.1747353802580035,0.016035634555191275
2021-01-28,0.7869690194825935,0.0,0.18360956904264975,0.01607344046136337
2021-01-29,1.0,0.0,0.1961699532145494,0.01615095845385069
2021-01-30,0.7502395400830406,0.0,0.20836087617808935,0.016230382626481143
2021-01-31,0.442031299904184,0.0,0.2157251149322383,0.016313301462707333
2021-02-01,0.5059086553816672,0.0,0.22264443335452064,0.01635809669607091
2021-02-02,0.45959757266049184,0.0,0.23046865250411935,0.016400244457013467
2021-02-03,0.4410731395720217,0.0,0.24005317869903547,0.016479527431114797
2021-02-04,0.5534972852123922,0.0,0.2503569883525362,0.016573106756289608
2021-02-05,0.4682210156499521,0.0,0.26091893649483505,0.016632233640358946
2021-02-06,0.42606196103481314,0.0,0.27339626534243977,0.016651613138480775
2021-02-07,0.44746087511977006,0.0,0.2793116148159794,0.016663544414191482
2021-02-08,0.6256786969019482,0.0,0.2855313152054104,0.016695843577727867
2021-02-09,0.5570105397636538,0.0,0.2935841085362108,0.01677900951138002
2021-02-10,0.6202491216863621,0.0,0.3023860937119735,0.01693062143202348
2021-02-11,0.5752155860747366,0.0,0.3126834717390231,0.017082868746047985
2021-02-12,0.6327052060044714,0.0,0.3240296548267835,0.017226714747589803
2021-02-13,0.47971893963589907,0.0,0.3355582261362507,0.01730670371433674
2021-02-14,0.5729798786330246,0.00024857071836937607,0.34041264838893714,0.01733219004884304
2021-02-15,0.8738422229319707,0.0,0.34572345144366856,0.01738662208181911
2021-02-16,0.7093580325774513,0.0,0.35385675920930165,0.01750099289040696
2021-02-17,0.5397636537847333,0.0,0.36488453708633295,0.0176663716676397
2021-02-18,0.5509421909932929,0.0,0.3753096212764555,0.017836339397068867
2021-02-19,0.5723411050782498,0.0,0.3836967797799193,0.017989116205577575
2021-02-20,0.5688278505269881,0.0,0.3918650247756573,0.018087143284419693
2021-02-21,0.6675183647396997,0.0,0.39479408803837907,0.018168332438664152
2021-02-22,0.6387735547748323,0.0,0.39863657459515495,0.018356479478717623
2021-02-23,0.560204407537528,0.0,0.40608336578922605,0.018701709882417983
2021-02-24,0.5519003513254551,0.0,0.4166513750115961,0.01922732140707017
2021-02-25,0.8153944426700734,0.0,0.4282178479222075,0.019838428641105004
2021-02-26,0.5697860108591505,0.0,0.44006715359107745,0.020426132218938062
2021-02-27,0.39252634940913445,0.0,0.45002084985076907,0.02096392211672692
2021-02-28,0.422868093260939,0.0,0.4541660463323253,0.021288855231866164
2021-03-01,0.6499520919833919,0.0,0.4580335977049416,0.02170327291483572
2021-03-02,0.6084318109230278,0.0,0.4625840925233137,0.02271393668665014
2021-03-03,0.613222612583839,0.0,0.46862818039627024,0.024328153571191433
2021-03-04,0.4525710635579687,0.0,0.47711837726094497,0.025956702106438488
2021-03-05,0.5560523794314916,0.0,0.48735432399172673,0.027281815002604938
2021-03-06,0.3538805493452571,0.0,0.4976691706338289,0.02805353556351468
2021-03-07,0.25806451612903225,0.0,0.5013857243566355,0.028346381312911225
2021-03-08,0.527307569466624,0.0,0.50581486523559,0.02915160122485488
2021-03-09,0.5697860108591505,0.0,0.5104909223415514,0.030859679831629225
2021-03-10,0.7920792079207921,0.0,0.5158690854346291,0.03337760260217593
2021-03-11,0.7604599169594379,0.0,0.5194781542396659,0.035289042390516515
2021-03-12,0.6180134142446503,0.0,0.5277388240895907,0.037709538175969864
2021-03-13,0.5413605876716704,0.0,0.5402115475750452,0.03908922430364151
2021-03-14,0.5138933248163526,0.0,0.5461634488022022,0.03955870941296817
2021-03-15,0.8907697221335037,0.0,0.5546333450187781,0.04064170213132473
2021-03-16,0.7141488342382626,0.0,0.5644990894484404,0.04301588479922646
2021-03-17,0.4573618652187799,0.0,0.5748059693433746,0.046385270000003885
2021-03-18,0.4816352603002236,0.0004971414367387521,0.586929849879751,0.0502180334737235
2021-03-19,0.40242733950814435,0.0,0.6012620809699422,0.05381379521631385
2021-03-20,0.5822420951772597,0.0,0.6196960752124455,0.056342307876544616
2021-03-21,0.5927818588310444,0.0,0.6274884009057742,0.057369986071118095
2021-03-22,0.9220696263174705,0.0,0.6345120546015971,0.05964614166017771
2021-03-23,0.6949856275950176,0.00024857071836937607,0.641433887445743,0.06460175113709823
2021-03-24,0.6129032258064516,0.0,0.6485488278890145,0.07229904775358964
2021-03-25,0.6595336953050144,0.0004971414367387521,0.6560138023232993,0.08041898686737191
2021-03-26,0.5528585116576173,0.00024857071836937607,0.6644618892386582,0.08855516381200305
2021-03-27,0.5091025231555414,0.00024857071836937607,0.6753999154889578,0.09695817067704024
2021-03-28,0.46151389332481635,0.00024857071836937607,0.6809472859396574,0.10025240296069311
2021-03-29,0.6368572341105079,0.0,0.6855452900974524,0.10494916023410293
2021-03-30,0.4899393165122964,0.0009942828734775043,0.6900052921963329,0.11347458622388784
2021-03-31,0.47013733631427657,0.0007457121551081282,0.694733305198276,0.12577580208089215
2021-04-01,0.4270201213669754,0.0004971414367387521,0.6978404953993359,0.14059727048417364
2021-04-02,0.3647396997764293,0.0004971414367387521,0.6996097220253694,0.1483431393946826
2021-04-03,0.3695305014372405,0.0014914243102162564,0.7015638512949736,0.15385955412963867
2021-04-04,0.31555413605876714,0.0007457121551081282,0.7022449155412313,0.1551709707684804
2021-04-05,0.3618652187799425,0.0012428535918468805,0.7026847011589764,0.1568343953404203
2021-04-06,0.38486106675183646,0.004225702212279393,0.70398418084569,0.1623714603602603
2021-04-07,0.3474928137975088,0.003231419338801889,0.7054079259076633,0.17582054965975213
2021-04-08,0.26700734589587993,0.0017399950285856326,0.7067551266745884,0.1901418222736232
2021-04-09,0.272436921111466,0.0014914243102162564,0.7083082718259427,0.20461763667761695
2021-04-10,0.20568508463749602,0.0012428535918468805,0.7102509141577699,0.22047444329567675
2021-04-11,0.23890130948578728,0.002982848620432513,0.7111627493959102,0.22601694445888787
2021-04-12,0.3024592781858831,0.002982848620432513,0.7117576086736438,0.2319028404449208
2021-04-13,0.312360268284893,0.002734277902063137,0.7126862243692739,0.24010905185999587
2021-04-14,0.3043755988502076,0.00571712652249565,0.7147450065236014,0.2503608416660763
2021-04-15,0.28393484509741296,0.007208550832711907,0.7172177154518766,0.2632542441542132
2021-04-16,0.25135739380389654,0.008451404424558787,0.71991113768458,0.2789361057946923
2021-04-17,0.21909932928776749,0.011185682326621925,0.7229351931585493,0.2957060433005763
2021-04-18,0.18428617055253912,0.004225702212279393,0.7247768880694521,0.3024863260696927
2021-04-19,0.3557968700095816,0.017399950285856326,0.7269435786231264,0.3095352390404614
2021-04-20,0.25934206323858194,0.013174248073576932,0.7291318138020322,0.3190416771109454
2021-04-21,0.3359948898115618,0.015659955257270694,0.7315792991088756,0.3309818130293555
2021-04-22,0.3417438518045353,0.019388516032811335,0.734294715915986,0.34329736053706555
2021-04-23,0.23826253593101246,0.017399950285856326,0.7371483729903979,0.35715112482101763
2021-04-24,0.15937400191632067,0.014168530947054437,0.7404653189100802,0.3744793613110453
2021-04-25,0.1526668795911849,0.02212279393487447,0.7420892648875941,0.38236198099700774
2021-04-26,0.33567550303417437,0.025105642555306985,0.7437305206745691,0.39070024833644307
2021-04-27,0.2551900351325455,0.025105642555306985,0.745941465053043,0.40154842542989744
2021-04-28,0.25998083679335676,0.03231419338801889,0.7486244325843135,0.415239987903522
2021-04-29,0.2618971574576813,0.02609992542878449,0.7515495257072506,0.4292295851724883
2021-04-30,0.21271159374001916,0.03529704200845141,0.7545810715116135,0.4418150689682451
2021-05-01,0.14595975726604918,0.03156848123291076,0.7578259196928064,0.4535969919347753
2021-05-02,0.10507824976045992,0.03181705195128014,0.7596885769417722,0.4583241774912107
2021-05-03,0.17087192590226766,0.036539895600298286,0.7612201510002966,0.46162003355794845
2021-05-04,0.2510380070265091,0.05070842654735272,0.7637852053799629,0.4688939816845034
2021-05-05,0.22452890450335355,0.06587124036788466,0.7666046398494312,0.48124641730794626
2021-05-06,0.2235707441711913,0.06388267462092966,0.7692614309257214,0.49615786447390997
2021-05-07,0.20121366975407218,0.06984837186179468,0.7721223930573372,0.5108081296606325
2021-05-08,0.13765570105397637,0.055182699478001494,0.7758037184064547,0.5250891606937041
2021-05-09,0.12871287128712872,0.061894108873974646,0.7777605473711124,0.5305886316051657
2021-05-10,0.264771638454168,0.11981108625403927,0.779709541926595,0.53693402290459
2021-05-11,0.19482593420632385,0.09942828734775043,0.7823364775517048,0.5477584080458875
2021-05-12,0.17725966145001598,0.10937111608252548,0.7863224184926993,0.5620122936617357
2021-05-13,0.13765570105397637,0.0686055182699478,0.7910200202238921,0.575307370665589
2021-05-14,0.14116895560523796,0.08376833209047974,0.7960093213588624,0.5873053626813064
2021-05-15,0.10284254231874801,0.0842654735272185,0.8016227814031771,0.5998483457197978
2021-05-16,0.07952730756946662,0.07680835197613721,0.8044619077659771,0.605310857916262
2021-05-17,0.18811881188118812,0.18568232662192394,0.8066140146728427,0.6124156855045625
2021-05-18,0.14500159693388695,0.20059656972408652,0.8107374018531819,0.62181396550174
2021-05-19,0.17885659533695306,0.20730797912005966,0.8170590289646842,0.6326657784573192
2021-05-20,0.15298626636857235,0.19786229182202336,0.8233315851484482,0.6456308038993527
2021-05-21,0.09517725966145002,0.16281382053194135,0.8284054502608316,0.6586966450911784
2021-05-22,0.08272117534334078,0.17176236639323889,0.8332911836826196,0.6771013791672231
2021-05-23,0.06802938358351965,0.16803380561769823,0.8360274834249972,0.6848652155905627
2021-05-24,0.10092622165442351,0.29778772060651254,0.8383513650465453,0.6939854050846367
2021-05-25,0.09453848610667519,0.3161819537658464,0.8425278991648016,0.7053534398626816
2021-05-26,0.10986905142127117,0.32861048968431517,0.8480174908478353,0.718261138701892
2021-05-27,0.1047588629830725,0.2923191648023863,0.8543014280989819,0.7308443633211829
2021-05-28,0.10795273075694667,0.3755903554561273,0.858992015916555,0.7439463160368313
2021-05-29,0.06802938358351965,0.4021874223216505,0.8631968703652754,0.7573141455828141
2021-05-30,0.05908655381667199,0.3731046482724335,0.8657854809076639,0.7634324190481475
2021-05-31,0.06930693069306931,0.4335073328361919,0.8676171445987109,0.7686419034804273
2021-06-01,0.08559565633982753,0.5824011931394482,0.8695091279468858,0.7781746750765922
2021-06-02,0.08623442989460237,0.6211782252050708,0.8729536212240496,0.7881017435653271
2021-06-03,0.060044714148834236,0.6132239622172508,0.877128037934421,0.7989947864914096
2021-06-04,0.09421909932928776,0.6619438230176485,0.8810232744151355,0.8103056358651606
2021-06-05,0.06739061002874482,0.6273924931643052,0.8857536165657524,0.8252986251816916
2021-06-06,0.05557329926541041,0.6189410887397464,0.888354190462692,0.8329703648644121
2021-06-07,0.07569466624081762,0.8883917474521501,0.8905588884879048,0.841233867582678
2021-06-08,0.05365697860108592,0.7874720357941835,0.8933270287513553,0.8499272845224844
2021-06-09,0.044714148834238264,0.6967437235893612,0.8972709656134282,0.8588417830592617
2021-06-10,0.0297029702970297,0.5575441213025105,0.9018691285768148,0.8676180482360297
2021-06-11,0.01916320664324497,0.4337559035545613,0.9065201208669356,0.8763553073191281
2021-06-12,0.016927499201533056,0.3348247576435496,0.9125850916751594,0.8861791243834434
2021-06-13,0.008942829766847652,0.29132488192890876,0.9158080511523397,0.8915240888044145
2021-06-14,0.009262216544235069,0.3338304747700721,0.9186771123691161,0.8972807881363019
2021-06-15,0.022037687639731716,0.6952522992791449,0.9226334360673172,0.9033125422029166
2021-06-16,0.015011178537208559,0.623912503107134,0.9271279783823242,0.9095643541771661
2021-06-17,0.009581603321622485,0.49540144171016653,0.9327092008915875,0.9156017208853133
2021-06-18,0.01341424465027148,0.6850608998260005,0.9378672859026027,0.9213436296712754
2021-06-19,0.0063877355477483235,0.3867760377827492,0.9445483430026088,0.9283495476899267
2021-06-20,0.0063877355477483235,0.46109868257519265,0.9481971661406893,0.9311337708863953
2021-06-21,0.009262216544235069,0.596818294804872,0.9513817475999512,0.9343933036315167
2021-06-22,0.009900990099009901,0.7991548595575442,0.9555178656951999,0.9386170811320041
2021-06-23,0.009900990099009901,0.686055182699478,0.960054809103107,0.942789144671201
2021-06-24,0.00830405621207282,0.7119065374098931,0.9651041916868184,0.9474591801229747
2021-06-25,0.00670712232513574,0.4742729306487696,0.9696391764924318,0.9520469674315356
2021-06-26,0.009581603321622485,0.7979120059656972,0.9751310179213434,0.9582717398874584
2021-06-27,0.006068348770360907,0.5846383296047726,0.9781309084778232,0.9617049115237661
2021-06-28,0.005110188438198659,0.540641312453393,0.9806936072412173,0.9652578901120329
2021-06-29,0.005748961992973491,1.0,0.983542420745092,0.9691595937675958
2021-06-30,0.0047908016608112424,0.4926671638081034,0.9864800859773472,0.9738878383129995
2021-07-01,0.0019163206643244969,0.30002485707183696,0.9894941366990591,0.9789364976206106
2021-07-02,0.0,0.002734277902063137,0.9920933607484719,0.9837332587522702
2021-07-03,0.0,0.0012428535918468805,0.995212990720857,0.9896178133522765
2021-07-04,0.002235707441711913,0.002734277902063137,0.9967851925431775,0.9926236476402002
2021-07-05,0.0,0.00024857071836937607,0.9982705012394115,0.9962877847705173
2021-07-06,0.0,0.00024857071836937607,1.0,1.0
Also, when I delete the stacked bar line of code, I get my two line plots displayed:
That's weird. Why can't I get all of the 3 plots displayed at once?

I saved your CSV data to file (thanks for adding that to your question) and find that your code appears to run as expected without modification:
It may be that you're encountering some kind of environment issue. Please update your question with your OS, pandas, and matplotlib versions. If you're running your code within an IDE (like Spyder or PyCharm), please list that as well.

Is focal length in pixel unit a linear measurment

I have a pan-tilt-zoom camera (changing focal length over time). There is no idea about its base focal length (e.g. focal length in time point 0). However, It is possible to track the change in focal length between frame and another based on some known constraints and assumptions (Doing a SLAM).
If I assume a random focal length (in pixel unit), for example, 1000 pixel. Then, the new focal lengths are tracked frame by frame. Would I get correct results relatively? Would the results (focal lengths) in each frame be correct up to scale to the ground truth focal length?
For pan and tilt, assuming 0 at start would be valid. Although it is not correct, The estimated values of new tili-pan will be correct up to an offset. However, I suspect the estimated focal length will not be even correct up to scale or offset.. Is it correct or not?

For a quick short answer - if pan-tilt-zoom camera is approximated as a thin lens, then this is the relation between distance (z) and focal length (f):
This is just an approximation. Not fully correct. For more precise calculations, see the camera matrix. Focal length is an intrinsic parameter in the camera matrix. Even if not known, it can be calculated using some camera calibration method such as DLT, Zhang's Method and RANSAC. Once you have the camera matrix, focal length is just a small part of it. You get many more useful things along with it.
OpenCV has an inbuilt implementation of Zhang's method. (Look at this documentation for explanations, but code is old and unusable. New up-to-date code below.) You need to take some pictures of a chess board through your camera. Here is some helper code:
import cv2
from matplotlib import pyplot as plt
import numpy as np
from glob import glob
from scipy import linalg
x,y = np.meshgrid(range(6),range(8))
world_points=np.hstack((x.reshape(48,1),y.reshape(48,1),np.zeros((48,1)))).astype(np.float32)
_3d_points=[]
_2d_points=[]
img_paths=glob('./*.JPG') #get paths of all checkerboard images
for path in img_paths:
im=cv2.imread(path)
ret, corners = cv2.findChessboardCorners(im, (6,8))
if ret: #add points only if checkerboard was correctly detected:
_2d_points.append(corners) #append current 2D points
_3d_points.append(world_points) #3D points are always the same
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(_3d_points, _2d_points, (im.shape[1],im.shape[0]), None, None)
print ("Ret:\n",ret)
print ("Mtx:\n",mtx)
print ("Dist:\n",dist)
You might want Undistortion: Correcting for Radial Distortion
# termination criteria
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((6*8,3), np.float32)
objp[:,:2] = np.mgrid[0:6,0:8].T.reshape(-1,2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d point in real world space
imgpoints = [] # 2d points in image plane.
for fname in img_paths:
img = cv2.imread(fname)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# Find the chess board corners
ret, corners = cv2.findChessboardCorners(gray, (6,8),None)
# If found, add object points, image points (after refining them)
if ret == True:
objpoints.append(objp)
cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)
imgpoints.append(corners)
if 'IMG_5456.JPG' in fname:
plt.figure(figsize=(20,10))
img_vis=img.copy()
cv2.drawChessboardCorners(img_vis, (6,8), corners, ret)
plt.imshow(img_vis)
plt.show()
#Calibration
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1],None,None)
# Reprojection Error
tot_error = 0
for i in range(len(objpoints)):
imgpoints2, _ = cv2.projectPoints(objpoints[i], rvecs[i], tvecs[i], mtx, dist)
error = cv2.norm(imgpoints[i],imgpoints2, cv2.NORM_L2)/len(imgpoints2)
tot_error += error
print ("Mean Reprojection error: ", tot_error/len(objpoints))
# undistort
mapx,mapy = cv2.initUndistortRectifyMap(mtx,dist,None,newcameramtx,(w,h),5)
dst = cv2.remap(img,mapx,mapy,cv2.INTER_LINEAR)
# crop the image
x,y,w,h = roi
dst = dst[y:y+h, x:x+w]
plt.figure(figsize=(20,10))
#cv2.drawChessboardCorners(dst, (6,8), corners, ret)
plt.imshow(dst)
plt.show()
# Reprojection Error
tot_error = 0
for i in range(len(objpoints)):
imgpoints2, _ = cv2.projectPoints(objpoints[i], rvecs[i], tvecs[i], mtx, dist)
error = cv2.norm(imgpoints[i],imgpoints2, cv2.NORM_L2)/len(imgpoints2)
tot_error += error
print ("Mean Reprojection error: ", tot_error/len(objpoints))

How to exchange colors between 2 images?

I have an image of spectacles with black background that I need to overlay onto a face image. To do so, I am taking the part of face image with shape same as spectacles; and put the colors of face image on black parts of the spectacles image. Then this small part of image can be put back. But I am not being able to take the correct colors from face image for the spectacles image. I tried this :
specs[np.where((hmd == [0,0,0,0]).all(axis=2))] = sub_face
specs image:
face image:
I need to put a resized specs image to face. I have resized specs image and also know the position where I will place the specs on face image. I just need to remove black background from specs and add relevant face colors so it looks like there are specs on face in a natural way.
Code I am using :
import cv2
specs = cv2.imread("rot_h0v0z0.png")
face = cv2.imread("~/Downloads/celebA/000001.png")
specs = cv2.resize(image, None, fx=0.3, fy=0.3, interpolation=cv2.INTER_AREA)
sub_face = face[0:specs.shape[0], 0:specs.shape[1]]
specs[np.where((hmd == [0,0,0,0]).all(axis=2))] = sub_face
Was able to solve it, turned out pretty simple :P
(b,g,r) = cv2.split(specs)
indices = np.where(b == [0])
for i,j in zip(indices[0], indices[1]):
specs[i,j] = sub_face[i,j]

Was able to solve it, turned out pretty simple :P
(b,g,r) = cv2.split(specs)
indices = np.where(b == [0])
for i,j in zip(indices[0], indices[1]):
specs[i,j] = sub_face[i,j]

journal quality kde plots with seaborn/pandas

I'm trying to do some comparative analysis for a publication. I came across seaborn and pandas and really like the ease with which I can create the analysis that I want. However, I find the manuals a bit scanty on the things that I'm trying to understand about the example plots and how to modify the plots to my needs. I'm hoping for some advice here on to get the plots I'm want. Perhaps pandas/seaborn is not what I need.
So, I would like to create subplots, (3,1) or (2,3), of the following figure:
Questions:
I would like the attached plot to have a title on the colorbar. Not sure if this is possible or exactly what is shown, i.e., is it relative frequency or occurrence or a percentage, etc? How can I put a explanatory tile on the colorbar (oriented vertically).
The text is a nice addition. The pearsonr is the correlation, but I'm not sure what is p. My guess is that it is showing the lag, or? If so, how can I remove the p in the text?
I would like to make the same kind of figure for different variables and put it all in a subplot.
Here's the code I pieced together from the seaborn manual/examples and from other users here on SO (thanks guys).
import netCDF4 as nc
import pandas as pd
import xarray as xr
import numpy as np
import seaborn as sns
import pdb
import matplotlib.pyplot as plt
from scipy import stats, integrate
import matplotlib as mpl
import matplotlib.ticker as tkr
import matplotlib.gridspec as gridspec
sns.set(style="white")
sns.set(color_codes=True)
octp = [622.0, 640.0, 616.0, 731.0, 668.0, 631.0, 641.0, 589.0, 801.0,
828.0, 598.0, 742.0,665.0, 611.0, 773.0, 608.0, 734.0, 725.0, 716.0,
699.0, 686.0, 671.0, 700.0, 656.0,686.0, 675.0, 678.0, 653.0, 659.0,
682.0, 674.0, 684.0, 679.0, 704.0, 624.0, 727.0,739.0, 662.0, 801.0,
633.0, 896.0, 729.0, 659.0, 741.0, 510.0, 836.0, 720.0, 685.0,430.0,
833.0, 710.0, 799.0, 534.0, 532.0, 605.0, 519.0, 850.0, 357.0, 858.0,
497.0,404.0, 456.0, 448.0, 836.0, 462.0, 381.0, 499.0, 673.0, 642.0,
641.0, 458.0, 809.0,562.0, 742.0, 732.0, 710.0, 658.0, 533.0, 811.0,
853.0, 856.0, 785.0, 659.0, 697.0,654.0, 673.0, 707.0, 711.0, 423.0,
751.0, 761.0, 638.0, 576.0, 538.0, 596.0, 718.0,843.0, 640.0, 647.0,
692.0, 599.0, 607.0, 537.0, 679.0, 712.0, 612.0, 641.0, 665.0,658.0,
722.0, 656.0, 656.0, 742.0, 505.0, 688.0, 805.0]
cctp = [482.0, 462.0, 425.0, 506.0, 500.0, 464.0, 486.0, 473.0, 577.0,
735.0, 390.0, 590.0,464.0, 417.0, 722.0, 410.0, 679.0, 680.0, 711.0,
658.0, 687.0, 621.0, 643.0, 690.0,630.0, 661.0, 608.0, 658.0, 624.0,
646.0, 651.0, 634.0, 612.0, 636.0, 607.0, 539.0,706.0, 614.0, 706.0,
401.0, 720.0, 746.0, 511.0, 700.0, 453.0, 677.0, 637.0, 605.0,454.0,
733.0, 535.0, 725.0, 668.0, 513.0, 470.0, 589.0, 765.0, 596.0, 749.0,
462.0,469.0, 514.0, 511.0, 789.0, 647.0, 324.0, 555.0, 670.0, 656.0,
786.0, 374.0, 757.0,645.0, 744.0, 708.0, 497.0, 654.0, 288.0, 705.0,
703.0, 446.0, 675.0, 440.0, 652.0,589.0, 542.0, 661.0, 631.0, 343.0,
585.0, 632.0, 591.0, 602.0, 365.0, 535.0, 663.0,561.0, 448.0, 582.0,
591.0, 535.0, 475.0, 422.0, 599.0, 594.0, 569.0, 576.0, 622.0,483.0,
539.0, 515.0, 621.0, 443.0, 435.0, 502.0, 443.0]
cctp = pd.Series(cctp, name='CTP [hPa]')
octp = pd.Series(octp, name='CTP [hPa]')
formatter = tkr.ScalarFormatter(useMathText=True)
formatter.set_scientific(True)
formatter.set_powerlimits((-2, 2))
g = sns.jointplot(cctp,octp, kind="kde",size=8,space=0.2,cbar=True,
n_levels=50,cbar_kws={"format": formatter})
# add a line x=y
x0, x1 = g.ax_joint.get_xlim()
y0, y1 = g.ax_joint.get_ylim()
lims = [max(x0, y0), min(x1, y1)]
g.ax_joint.plot(lims, lims, ':k')
plt.show()
plt.savefig('test_fig.png')
I know I'm asking a lot here. So I put the questions in order of priority.

1: To set the colorbar label, you can add the label key to the cbar_kws dict:
cbar_kws={"format": formatter, "label": 'My colorbar'}
2: To change the stats label, you need to first slightly modify the stats.pearsonr function to only return the first value, instead of the (pearsonr, p) tuple:
pr = lambda a, b: stats.pearsonr(a, b)[0]
Then, you can change that function using jointplot's stat_func kwarg:
stat_func=pr
and finally, you need to change the annotation to get the label right:
annot_kws={'stat':'pearsonr'})
Putting that all together:
pr = lambda a, b: stats.pearsonr(a, b)[0]
g = sns.jointplot(cctp,octp, kind="kde",size=8,space=0.2,cbar=True,
n_levels=50,cbar_kws={"format": formatter, "label": 'My colorbar'},
stat_func=pr, annot_kws={'stat':'pearsonr'})
3: I don't think its possible to put everything in a subplot with jointplot. Happy to be proven wrong there though.