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14 | 14 | ax = fig.add_axes([0.05, 0.05, 0.2, 01], polar=True) |
15 | 15 | ax.axesPatch.set_alpha(axalpha) |
16 | 16 | N = 20 |
17 | | -theta = np.arange(0.0, 2*np.pi, 2*np.pi/N) |
| 17 | +theta = np.arange(0.0, 2*np.pi, 2*np.pi/N) + np.pi |
18 | 18 | radii = 10*np.random.rand(N) |
19 | | -width = np.pi/4*np.random.rand(N) |
| 19 | +width = np.pi/6*np.random.rand(N) |
| 20 | +#radii = np.log(np.arange(1,N+1)) |
| 21 | +#width = np.arange(N, dtype=float)/N*np.pi/8 |
| 22 | + |
20 | 23 | bars = ax.bar(theta, radii, width=width, bottom=0.0) |
21 | 24 | for r,bar in zip(radii, bars): |
22 | 25 | bar.set_facecolor( cm.jet(r/10.)) |
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33 | 36 | x = mu + sigma*np.random.randn(10000) |
34 | 37 |
|
35 | 38 | # the histogram of the data |
36 | | -n, bins, patches = axhist.hist(x, 50, normed=1, facecolor='green', edgecolor='green', alpha=0.75) |
| 39 | +n, bins, patches = axhist.hist(x, 50, normed=1, |
| 40 | + facecolor='green', edgecolor='green', alpha=0.75) |
37 | 41 |
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38 | 42 |
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39 | 43 | y = mlab.normpdf( bins, mu, sigma) |
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51 | 55 |
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52 | 56 | #the math background |
53 | 57 | tex = r"$W^{3\beta}_{\delta_1 \rho_1 \sigma_2} = U^{3\beta}_{\delta_1 \rho_1} + \frac{1}{8 \pi 2} \int^{\alpha_2}_{\alpha_2} d \alpha^\prime_2 \left[\frac{ U^{2\beta}_{\delta_1 \rho_1} - \alpha^\prime_2U^{1\beta}_{\rho_1 \sigma_2} }{U^{0\beta}_{\rho_1 \sigma_2}}\right]$" |
| 58 | +radargreen = '#d5de9c' |
| 59 | +orange = '#ee8d18' |
54 | 60 | axback.text(0.5, 0.5, tex, |
55 | | - transform=axback.transAxes, color="0.5", alpha=0.5, fontsize=40, |
| 61 | + transform=axback.transAxes, color='black', alpha=0.25, fontsize=40, |
56 | 62 | ha='center', va='center') |
57 | 63 | axback.set_axis_off() |
58 | 64 |
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