# -*- coding: utf-8 -*- """Plot to demonstrate the qualitative1 colormap. """ import numpy as np import matplotlib.pyplot as plt from typhon.plots import (figsize, cmap2rgba) x = np.linspace(0, 10, 100) fig, ax = plt.subplots(figsize=figsize(10)) ax.set_prop_cycle(color=cmap2rgba('qualitative1', 7)) for c in np.arange(1, 8): ax.plot(x, (15 + x) * c, linewidth=3) ax.set_xlim(x.min(), x.max()) fig.tight_layout() plt.show()
data = csv.read('/home/lukas/Desktop/2016/35/CL.txt') csv.read('/home/lukas/Desktop/2016/35/MASTER.txt', stack=False, output=data) data['PYR_CBH'] = clb.estimate_cloud_height(data['L'], data['TT002'] + 273.15) # 10 minute moving average _, data['PYR_CBH'] = clb.math.moving_average(data['MPLTIME'], data['PYR_CBH'], 10) _, data['CL_WBU'] = clb.math.moving_average(data['MPLTIME'], data['CL_WBU'], 10) # Plots plt.style.use('typhon') fig, axes = plt.subplots(4, 2, figsize=figsize(15), sharex=True, sharey=True) for cc, ax in zip(np.arange(1, 9), axes.ravel()): # mask = np.logical_and(data['CL_SCBG'] >= cc , data['CL_SCBG'] < cc + 1) mask = np.logical_and(np.ma.greater_equal(data['CL_SCBG'], cc), np.ma.less(data['CL_SCBG'], cc + 1)) sts = clb.math.compare_arrays(data['CL_WBU'][mask], data['PYR_CBH'][mask]) ax.plot(data['MPLTIME'][mask], data['CL_WBU'][mask] / 1e3, color='darkblue', label='Ceilometer', linestyle='none', marker='.') ax.plot(data['MPLTIME'][mask], data['PYR_CBH'][mask] / 1e3,
# -*- coding: utf-8 -*- """Plot to demonstrate the vorticity colormap. """ from mpl_toolkits.mplot3d import axes3d import matplotlib.pyplot as plt from typhon.plots import figsize fig = plt.figure(figsize=figsize(10)) ax = fig.gca(projection='3d') X, Y, Z = axes3d.get_test_data(0.05) surf = ax.plot_surface(X, Y, Z, cmap='vorticity', vmin=-80, vmax=80, rstride=1, cstride=1, linewidth=0) fig.colorbar(surf) fig.tight_layout() plt.show()
# -*- coding: utf-8 -*- """Plot to demonstrate the phase colormap """ import numpy as np import matplotlib.pyplot as plt from typhon.cm import mpl_colors from typhon.plots import figsize x = np.linspace(0, 4 * np.pi, 200) phase_shifts = np.linspace(0, 2 * np.pi, 10) fig, ax = plt.subplots(figsize=figsize(10)) ax.set_prop_cycle(color=mpl_colors('phase', len(phase_shifts))) for p in phase_shifts: ax.plot(x, np.sin(x + p), lw=2) ax.set_xlim(x.min(), x.max()) ax.set_ylim(-1.2, 1.2) fig.tight_layout() plt.show()
data = csv.read('/home/lukas/Desktop/2016/35/CL.txt') csv.read('/home/lukas/Desktop/2016/35/MASTER.txt', stack=False, output=data) data['PYR_CBH'] = clb.estimate_cloud_height(data['L'], data['TT002']+273.15) # 10 minute moving average _, data['PYR_CBH'] = clb.math.moving_average( data['MPLTIME'], data['PYR_CBH'], 10) _, data['CL_WBU'] = clb.math.moving_average( data['MPLTIME'], data['CL_WBU'], 10) # Plots plt.style.use('typhon') fig, axes = plt.subplots(4, 2, figsize=figsize(15), sharex=True, sharey=True) for cc, ax in zip(np.arange(1, 9), axes.ravel()): # mask = np.logical_and(data['CL_SCBG'] >= cc , data['CL_SCBG'] < cc + 1) mask = np.logical_and(np.ma.greater_equal(data['CL_SCBG'], cc), np.ma.less(data['CL_SCBG'], cc + 1)) sts = clb.math.compare_arrays(data['CL_WBU'][mask], data['PYR_CBH'][mask]) ax.plot( data['MPLTIME'][mask], data['CL_WBU'][mask]/1e3, color='darkblue', label='Ceilometer', linestyle='none', marker='.') ax.plot(
# -*- coding: utf-8 -*- """Plot to demonstrate the vorticity colormap. """ from mpl_toolkits.mplot3d import axes3d import matplotlib.pyplot as plt import numpy as np import typhon.cm from typhon.plots import figsize fig = plt.figure(figsize=figsize(10)) ax = fig.gca(projection='3d') X, Y, Z = axes3d.get_test_data(0.05) surf = ax.plot_surface(X, Y, Z, cmap='vorticity', vmin=-80, vmax=80, rstride=1, cstride=1, linewidth=0) fig.colorbar(surf) fig.tight_layout() plt.show()
def test_figsize(self): """Test golden ratio for figures sizes.""" ret = plots.figsize(10) assert ret == (10, 6.1803398874989481)