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import numpy as np from scipy import stats import matplotlib.pyplot as plt
f1 = open('/home/user/Downloads/Rain_WCI_MAM.txt','r') lines1 = f1. readlines() #reads and closes the file f1. close() rain_mam = np. genfromtxt(lines1)
rain_mam[:,1]
array([30.7, 13.1, 20.8, 12.2, 20.2, 20.8, 27.1, 42.9, 9.2, 4.3, 31.6, 9.5, 40.5, 5.6, 28.9, 24.2, 2.1, 5.6, 30.8, 9.7, 29.8, 13.1, 31.6, 10.7, 13.6, 5.1, 10.4, 12.7, 11.3, 24.5, 13.6, 11. , 16.8, 42.7, 7.7, 12.3, 28.4, 12.8, 28.5, 3.4, 39.9, 9.8, 34.9, 18. ,
- , 14.3, 94. , 16.5, 18.3, 49.8, 0.5, 4.3, 19.1, 8.6, 13. , 53.5, 12.1, 3.7, 11.5, 16.1, 14.5, 16. , 64.1, 17.4, 17.8, 10.1, 10.4, 9.1, 12.4, 16.9, 10.5, 18.3, 12.1, 33.1, 18.8, 10.4, 21.4, 11.6, 4.8, 10.1, 26.2, 7.8, 5.1, 4.7, 12.7, 18.1, 33.1, 4.7, 20.1, 13.2, 10. , 17.6, 12.4, 20. , 17.8, 19. , 60.6, 10. , 11.7, 10.8, 27.4, 6.2, 16.6, 24. , 6.6, 15.7, 29.5, 17.1, 33.4, 14.9, 21.3, 97.7, 74.7, 3.9, 19.6, 25.4, 37.7, 15.3, 6. , 24.4, 13.1, 10.4, 13.3, 15.6, 12. , 18.6, 38.6, 20.5, 43.1, 21.6, 40.4, 17.5, 4.9, 21.4, 27. , 27.7, 28.9, 33.7, 14.7, 2.8, 13.4, 18.4, 19.3, 28.9, 64.6])
f2 = open('/home/user/Downloads/Rain_WCI_ANN.txt','r') lines2 = f2. readlines() #reads and closes the file f2. close() rain_ann = np. genfromtxt(lines2)
rain_mam[:,1] / rain_ann[:,1]
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array([0.05853194, 0.02255121, 0.03761302, 0.02488272, 0.03242896, 0.04179224, 0.07910099, 0.05421458, 0.01617156, 0.00793944, 0.04939043, 0.0159637 , 0.0796147 , 0.00794326, 0.05361781, 0.04143126, 0.00373068, 0.01228609, 0.05296647, 0.02062513, 0.06486722, 0.01787908, 0.04194876, 0.01354259, 0.03116407, 0.00973097, 0.01961154, 0.0267989 , 0.06438746, 0.03852201, 0.04466338, 0.0248083 , 0.03126163, 0.11236842, 0.02589106, 0.02239621, 0.05176814, 0.01801295, 0.04291522, 0.00586106, 0.13594549, 0.0167952 , 0.06400147, 0.02995507, 0.08682171, 0.02256944, 0.08891411, 0.08470226, 0.03273118, 0.11054384, 0.00097106, 0.00928325, 0.04172128, 0.01540666, 0.03501212, 0.07004451, 0.01907314, 0.00754948, 0.0245779 , 0.03255814, 0.02543413, 0.03410787, 0.08204275, 0.03092784, 0.03473171, 0.02288174, 0.01790634, 0.02304965, 0.04123711, 0.03442656, 0.02271252, 0.02719168, 0.02387059, 0.0435641 , 0.02571468, 0.01757647, 0.03743222, 0.02700186, 0.00952948, 0.01557921, 0.07806913, 0.01547005, 0.00840475, 0.00737602, 0.01968992, 0.02355544, 0.06354387, 0.00737255, 0.02421979, 0.028903 , 0.01424907, 0.03436158, 0.02485966, 0.0337325 , 0.04447776, 0.04270623, 0.09170702, 0.0269179 , 0.03003851, 0.0157251 , 0.0468216 , 0.01871416, 0.02480203, 0.06582556, 0.00880822, 0.02239977, 0.04289661, 0.02700142, 0.05461086, 0.02850038, 0.03733567, 0.19007782, 0.10258171, 0.00777977, 0.04541242, 0.06536284, 0.1423716 , 0.02331962, 0.01247142, 0.03603604, 0.03775216, 0.01846263, 0.02664263, 0.02116977, 0.02149767, 0.03037727, 0.05831697, 0.03251388, 0.10353111, 0.05444921, 0.07288472, 0.05589269, 0.00810721, 0.04401481, 0.05004634, 0.04102488, 0.04440688, 0.0593101 , 0.03320533, 0.00362506, 0.02038333, 0.0351682 , 0.02481039, 0.0590519 , 0.11954108])
f3 = open('/home/user/Downloads/Nino3.4_1871_2015.txt','r') lines3 = f3. readlines() #reads and closes the file f3. close() nino = np. genfromtxt(lines3)
nino
array([[ 1.871e+03, -2.500e-01, -5.800e-01, ..., -3.300e-01, -3.100e-01, -5.800e-01], [ 1.872e+03, -7.200e-01, -6.200e-01, ..., -9.400e-01, -7.900e-01, -8.800e-01], [ 1.873e+03, -7.800e-01, -1.010e+00, ..., -7.800e-01, -7.700e-01, -7.000e-01], ..., [ 2.013e+03, -4.200e-01, -4.000e-01, ..., -2.400e-01, -2.000e-02, -9.000e-02], [ 2.014e+03, -4.200e-01, -4.500e-01, ..., 4.800e-01, 8.900e-01, 7.700e-01], [ 2.015e+03, 5.900e-01, 5.700e-01, ..., 2.210e+00, 2.570e+00, 2.560e+00]])
nino_mam = nino[:,1:3]. mean(axis = 1)
nino_mam
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## mean line plt. plot(rain_mam[:,0],rain_mam[:,1] ***** 0 + rain_mam[:,1]. mean(),color = 'k', li
##standard deviation plt. fill_between(rain_mam[:,0], rain_mam[:,1] ***** 0 + rain_mam[:,1]. mean() - rain rain_mam[:,1] ***** 0 + rain_mam[:,1]. mean() + rain_mam[:,1]. std(d
plt. ylabel('Rainfall (mm)',size = 12) plt. xlim(1870,2016) plt. title("MAM Rainfall Trend with Mean and Standard Deviation", fontsize plt. text(1990,120,"Mean={:.2f} mm". format(rain_mam[:,1]. mean()),fontsize = plt. text(1990,110,"STD={:.2f} mm". format(rain_mam[:,1]. std(ddof = 1)),fonts
Text(1990, 110, 'STD=15.88 mm')
ig = plt. figure(figsize = [12, 3]) rain_p = rain_mam[:,1] / rain_ann[:,1] ***** 100 plt. plot(rain_mam[:,0],rain_p,color = 'purple', linewidth = 1,linestyle = '-') ## mean line plt. plot(rain_mam[:,0],rain_p ***** 0 + rain_p. mean(),color = 'k', linewidth = 1)
plt. ylabel('%',size = 12)
Text(0, 0.5, '%')
x = rain_mam[:,1]
fig = plt. figure(figsize = [12, 5]) fig. subplots_adjust(hspace = 0.50,wspace = 0.25)
plt. subplot(2,2,1)
#num_bins = 11 #change to desored bins bins = np. arange(0,75,5)
n, bins, patches = plt. hist(x, bins,
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color = 'violet',rwidth = 0.8, alpha = 0.7)
plt. xlabel('Rainfall (in mm)') plt. ylabel('Number')
plt. subplot(2,2,2)
n, bins, patches = plt. hist(x, bins, density = 1, color = 'k',rwidth = 0.8, alpha = 0.7)
plt. xlabel('Rainfall (mm)') plt. ylabel('Probability')
Text(0, 0.5, 'Probability')
x = rain_ann[:,1]
fig = plt. figure(figsize = [12, 5]) fig. subplots_adjust(hspace = 0.50,wspace = 0.25)
plt. subplot(2,2,1)
#num_bins = 11 #change to desored bins bins = np. arange(500,1200,50)
n, bins, patches = plt. hist(x, bins, color = 'violet',rwidth = 0.8, alpha = 0.7)
plt. xlabel('Rainfall (in mm)') plt. ylabel('Number')
plt. subplot(2,2,2)
n, bins, patches = plt. hist(x, bins, density = 1, color = 'k',rwidth = 0.8, alpha = 0.7)
plt. xlabel('Rainfall (mm)') plt. ylabel('Probability')
Text(0, 0.5, 'Probability')
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perc(nino_mam)
{1: -1.75, 5: -1.42, 10: -1.09, 25: -0.67, 50: -0.12, 75: 0.46, 90: 1.24, 95: 1.41, 99: 2.29}
x = rain_mam[:,1]
fig = plt. figure(figsize = [8, 4]) fig. subplots_adjust(hspace = 0.50,wspace = 0.25)
plt. subplot(1,1,1)
#num_bins = 11 bins = np. arange(0,75,5)
n, bins, patches = plt. hist(x, bins, color = 'green',rwidth = 0.8, alpha = 0.7)
plt. xlabel('Rainfall (cm)') plt. ylabel('Number')
color = ['red','tomato','blue','mediumblue','k','deeppink','olivedrab','ora
perc = [1,5,10,25,50,75,90,95,99] yy = [36,32,28,24,20,16,12,8,4] for i in np. arange(9):
plt. plot([np. percentile(rain_mam[:,1], perc[i]),np. percentile(rain_ma plt. text(90,yy[i],"{}th ={:.2f}". format(perc[i],np. percentile(rain_ma plt. ylim(0,40)
(0.0, 40.0)
x = rain_ann[:,1]
fig = plt. figure(figsize = [8, 4]) fig. subplots_adjust(hspace = 0.50,wspace = 0.25)
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plt. subplot(1,1,1)
#num_bins = 12 bins = np. arange(250,1000,50)
n, bins, patches = plt. hist(x, bins, color = 'green',rwidth = 0.8, alpha = 1)
plt. xlabel('Rainfall (cm)') plt. ylabel('Number')
color = ['red','tomato','gold','mediumblue','k','deeppink','olivedrab','ora perc = [1,5,10,25,50,75,90,95,99] yy = [36,32,28,24,20,16,12,8,4] for i in np. arange(9):
plt. plot([np. percentile(rain_ann[:,1], perc[i]),np. percentile(rain_an plt. text(1000,yy[i],"{}th ={:.2f}". format(perc[i],np. percentile(rain_ plt. ylim(0,40)
(0.0, 40.0)
import numpy as np import matplotlib.pyplot as plt
# Example data nino_mam = np. random. normal(0, 1, 1000)
fig = plt. figure(figsize = [8, 4]) fig. subplots_adjust(hspace = 0.50, wspace = 0.25)
plt. subplot(1, 1, 1)
# Define bins bins = np. arange( - 2.5, 2.5, 0.2)
# Create histogram n, bins, patches = plt. hist(nino_mam, bins, color = 'green', rwidth = 0.8, al
plt. xlabel('Nino 3.4 ($^o$ C)') plt. ylabel('Number')
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yy = [0.36,0.32,0.28,0.24,0.20,0.16,0.12,0.08,0.04] for i in np. arange(9):
plt. plot([np. percentile(rain_mam[:,1], perc[i]),np. percentile(rain_ma plt. text(90,yy[i] / 10,"{}th ={:.2f}". format(perc[i],np. percentile(rain #plt.ylim(0,0)
import matplotlib import numpy as np import matplotlib.pyplot as plt
annual = rain_mam x = rain_mam[:,1]
fig = plt. figure(figsize = [12, 5]) fig. subplots_adjust(hspace = 0.50,wspace = 0.25)
kde = stats. gaussian_kde(annual[:,1]) method = ['scott','silverman',kde. factor / 2,kde. factor / 4] Title = ["Scott","Silverman","Silverman/2","Silverman/4"] for i in range(4):
plt. subplot(2,2,i + 1)
num_bins = 11 bins = np. arange(70,141,5)
n, bins, patches = plt. hist(x, num_bins, color = 'violet',rwidth = 0.8, alpha = 0.7,density =True )
kde = stats. gaussian_kde(annual[:,1],bw_method = method[i]) xx = np. linspace(0,75, 75) plt. plot(xx, kde(xx),color = 'black',linewidth = 1) plt. xlabel('Rainfall (mm)') plt. ylabel('Probalities') plt. title(Title[i])
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annual = rain_ann x = rain_ann[:,1]
fig = plt. figure(figsize = [12, 5]) fig. subplots_adjust(hspace = 0.50,wspace = 0.25)
kde = stats. gaussian_kde(annual[:,1]) method = ['scott','silverman',kde. factor / 2,kde. factor / 4] Title = ["Scott","Silverman","Silverman/2","Silverman/4"] for i in range(4):
plt. subplot(2,2,i + 1)
num_bins = 11 bins = np. arange(70,141,5)
n, bins, patches = plt. hist(x, num_bins, color = 'violet',rwidth = 0.8, alpha = 0.7,density =True )
kde = stats. gaussian_kde(annual[:,1],bw_method = method[i]) xx = np. linspace(0,1000, 25) plt. plot(xx, kde(xx),color = 'black',linewidth = 1) plt. xlabel('Rainfall (mm)') plt. ylabel('Probalities') plt. title(Title[i])
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#boxplot with percentile fig = plt. figure(figsize = [5, 5]) fig. subplots_adjust(hspace = 0.60,wspace = 0.55)
plt. subplot(1,2,1)
plt. boxplot(rain_mam[:,1],meanline =True ,showmeans =True ,whis = (5,95)) plt. ylabel("Rainfall (mm)") plt. xticks([1],["MAM"])
plt. subplot(1,2,2) plt. boxplot(rain_ann[:,1],meanline =True ,showmeans =True ,whis = (5,95)) plt. ylabel("Rainfall (mm)") plt. xticks([1],["Annual"])
([<matplotlib.axis.XTick at 0x79c6c4b3f8f0>], [Text(1, 0, 'Annual')])
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plt. scatter(rain_mam[:,1],rain_ann[:,1],color = 'mediumblue') plt. xlabel("March-April-May rainfall (cm)", size = 12) plt. ylabel("Annual Rainfall (cm)", size = 12)
Text(0, 0.5, 'Annual Rainfall (cm)')
plt. scatter(nino_mam,rain_mam[:,1],color = 'blue') plt. xlabel("Nino3.4($^{o}C$)",size = 12)
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plt. subplot(3,1,3)
x_value = np. convolve(rain_ann[:,0], np. ones(N) / N, mode = 'valid') y_value = np. convolve(nino_mam, np. ones(N) / N, mode = 'valid') plt. plot(x_value,y_value,color = 'blue', linewidth = 1,linestyle = '-') ## mean line plt. plot(x_value,y_value ***** 0 + nino_mam. mean(),color = 'k', linewidth = 1, linest plt. ylabel("$^o$ C", size = 12) plt. title("(c) Nino 3.4",loc = 'left')
Text(0.0, 1.0, '(c) Nino 3.4')
##lines plots of running mean fig = plt. figure(figsize = [12, 8]) fig. subplots_adjust(hspace = 0.35,wspace = 0.45)
N = 30
plt. subplot(3,1,1) x_value = np. convolve(rain_mam[:,0], np. ones(N) / N, mode = 'valid') y_value = np. convolve(rain_mam[:,1], np. ones(N) / N, mode = 'valid') plt. plot(x_value,y_value,color = 'violet', linewidth = 1,linestyle = '-') ## mean line plt. plot(x_value,y_value ***** 0 + rain_mam[:,1]. mean(),color = 'k', linewidth = 1, l plt. ylabel('mm',size = 12) plt. title("(a) MAM Summer(WCI) Rainfall",loc = 'left') plt. subplot(3,1,2)
x_value = np. convolve(rain_ann[:,0], np. ones(N) / N, mode = 'valid') y_value = np. convolve(rain_ann[:,1], np. ones(N) / N, mode = 'valid') plt. plot(x_value,y_value,color = 'purple', linewidth = 1,linestyle = '-') ## mean line plt. plot(x_value,y_value ***** 0 + rain_ann[:,1]. mean(),color = 'k', linewidth = 1, l
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plt. ylabel('mm',size = 12) plt. title("(b) All India Annual (WCI) rainfall",loc = 'left')
plt. subplot(3,1,3)
x_value = np. convolve(rain_ann[:,0], np. ones(N) / N, mode = 'valid') y_value = np. convolve(nino_mam, np. ones(N) / N, mode = 'valid') plt. plot(x_value,y_value,color = 'blue', linewidth = 1,linestyle = '-') ## mean line plt. plot(x_value,y_value ***** 0 + nino_mam. mean(),color = 'k', linewidth = 1, linest plt. ylabel("$^o$ C", size = 12) plt. title("(c) Nino 3.4",loc = 'left')
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