Jupyter

jupyter.template.py

%%writefile this_cell.txt
でそのセルの内容をそのまま出力。コードをターミナルで直接動かしたいときに使える。

%matplotlib inline
#%matplotlib notebook

from IPython.display import display, HTML 
display(HTML("<style>.container { width:80% !important; }</style>")) 

import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
import matplotlib.patches as mpatches
import numpy as np

plt.rc('text', usetex=True)  # To use bold font in LaTex
plt.rcParams['text.latex.preamble']=[r"\usepackage{amsmath}"] #To use bold font in LaTex
plt.rcParams['figure.autolayout']=True
plt.rcParams['figure.figsize'] = 5, 4
plt.rcParams['figure.dpi'] = 300
plt.rcParams['font.family'] ='sans-serif' #使用するフォントファミリー
plt.rcParams['font.serif'] = ['Tahoma']
plt.rcParams['font.sans-serif'] = ['Arial', 'Helvetica']  #使用するフォント(探す順序)
plt.rcParams['xtick.direction'] = 'in' #x軸の目盛線が内向き('in')か外向き('out')か双方向か('inout')
plt.rcParams['ytick.direction'] = 'in' #y軸の目盛線が内向き('in')か外向き('out')か双方向か('inout')
plt.rcParams['xtick.major.width'] = 1.0 #x軸主目盛り線の線幅
plt.rcParams['ytick.major.width'] = 1.0 #y軸主目盛り線の線幅
plt.rcParams['font.size'] = 8 #フォントの大きさ
plt.rcParams['axes.linewidth'] = 1.0# 軸の線幅edge linewidth。囲みの太さ
plt.rcParams['savefig.dpi'] = 300
plt.rcParams['savefig.bbox'] = 'tight'
plt.rcParams['savefig.pad_inches'] = 0.05
plt.rcParams['savefig.transparent'] = True


## Output multiple-page PDF
from matplotlib.backends.backend_pdf import PdfPages
pdf = PdfPages('xxxx.pdf')
for _ in range(npages):
    ....
    pdf.savefig()
pdf.close()


## To prevent memory leak
%matplotlib
plt.ioff()


## Not draw top and right frame lines.
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)

## Ticks
ax.set_ylim(0.0, 6.0)
ax.set_yticks([0,1,2,3,4,5])
ax.yaxis.set_ticks_position('both')

# densely dotted
linestyle=(0,(1,1))
    # https://matplotlib.org/gallery/lines_bars_and_markers/linestyles.html

## Log scale
ax.set_xscale('log')
ax.set_xticks([0.1, 1, 10])
ax.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter()) # This set ticklabels automatically.
#ax.set_xticklabels(('0.1','1','10')) # If you want to set labels manually.
ax.set_yticks([0,0.1,0.2,0.3])


## Text
ax.text(-0.25, 1.05, '(a)', transform=ax.transAxes, size=10, weight='bold')


## Legend
ax.legend(fontsize=8, loc='upper left', bbox_to_anchor=(0.1, 0.95))
# This puts the upper left corner of the legend box into (0.1,0.95)


## Vertical line
ax.axvline(x=0, ymin=0, ymax=1, **kwargs)


## Convert format
$ ipython nbconvert notebook.ipynb
This will be html (default)

$ ipython nbconvert --to FORMAT notebook.ipynb
FORMAT can be latex, pdf, slides, markdown, etc.


## Moving average
import numpy as np
N_MOVING_AVERAGE = 200  # Window size
ONES_MOVING_AVERAGE = np.ones((N_MOVING_AVERAGE,))/N_MOVING_AVERAGE
ax.plot(np.convolve(x, ONES_MOVING_AVERAGE, mode='valid'),
        np.convolve(y, ONES_MOVING_AVERAGE, mode='valid'),)

##################################################################
## 2D histogram heatmap
ncols=1
nrows=1
fig, axs = plt.subplots(ncols=ncols, nrows=nrows, figsize=(3*ncols, 2.25*nrows), dpi=600)
ax = axs

etas = []
thetas = []
for l in open('angles.dat'):
    lsp = l.split()
    etas.append(float(lsp[1]))
    thetas.append(float(lsp[2]))

print('Number of data points: %i' % len(etas))

H, xedges, yedges = np.histogram2d(etas,  thetas, bins=(180, 180),
                                    range=[[0.,360.0],[0.,360.0]], density=True)

# H needs to be rotated and flipped
H = np.rot90(H)
H = np.flipud(H)

# Mask zeros
Hmasked = np.ma.masked_where(H==0,H) # Mask pixels with a value 

ax.set_xlabel(r'$\eta$')
ax.set_ylabel(r'$\theta$')
ax.pcolormesh(xedges, yedges, Hmasked, cmap='rainbow')
ax.set_aspect('equal', adjustable='box')

fig.savefig('sample_2d_histogram_heatmap.png')
fig.savefig('sample_2d_histogram_heatmap.svg')
##################################################################


# Contact map
import pandas as pd
import seaborn as sns

cor_plots = range(13,207+1) # 13 ~ 207  (195)
df = pd.DataFrame(index=cor_plots, columns=cor_plots,dtype=float)

for cut in [7.3, 6.3]:
    for K in [50, ]:
        for Mg in Mg_calc:

            for i in cor_plots:
                df[i][i] = 1.0

            for l in open('./Mg_nt_nt_correlations/%i_%03i.K_bound_%3.1f.cor' % (K, Mg,cut)):
                lsp = l.split()
                i = int(lsp[0])
                j = int(lsp[1])
                c = float(lsp[2])
                df[i][j] = c
                df[j][i] = c

            # Set up the matplotlib figure
            f, ax = plt.subplots(figsize=(4, 4))

            # Generate a custom diverging colormap
            #cmap = sns.diverging_palette(220, 20, n=24, as_cmap=False)
            cmap = sns.color_palette("RdBu_r", 24)
            #cmap = sns.diverging_palette(220, 20, n=24)

            # Draw the heatmap with the mask and correct aspect ratio
            sns.heatmap(df, cmap=cmap, vmin=-1.0, vmax=1.0, center=0., 
                        square=True, linewidths=.0, cbar_kws={"shrink": .8})
            
            ax.hlines(np.arange(20-12-0.5,210-12-0.5,10), *ax.get_ylim(),linewidths=0.1)
            ax.vlines(np.arange(20-12-0.5,210-12-0.5,10), *ax.get_xlim(),linewidths=0.1)
            ax.set_xticks(np.arange(20-12-0.5,210-12-0.5,10))
            ax.set_xticklabels(np.arange(20,210,10))            
            ax.set_yticks(np.arange(20-12-0.5,210-12-0.5,10))
            ax.set_yticklabels(np.arange(20,210,10))    
            ax.tick_params(direction='out', width=0.5)
            
            f.savefig('%i_%03i.K_bound_%3.1f.png' % (K, Mg,cut))

###################################################################                     
def draw_contact_map(H):
    ''' H is (N+1)x(N+1) matrix where index 0 is not used for data.'''

    N = H.shape[0] - 1
    
    fig, axs = plt.subplots(1, 1, figsize=(3*1, 2.25*1), dpi=300)
    ax = axs

    edges = np.arange(0.5, N+0.5, 1.0)

    # H needs to be rotated and flipped
    H = np.rot90(H)
    H = np.flipud(H)

    hm = ax.pcolormesh(edges, edges,  H[1:,1:], cmap='Greys')
    ax.set_aspect('equal', adjustable='box')

    ax.hlines(np.arange(10,N+1,10), *ax.get_ylim(),linewidths=0.1)
    ax.vlines(np.arange(10,N+1,10), *ax.get_xlim(),linewidths=0.1)

    ax.plot([0, 1], [0, 1], transform=ax.transAxes, linewidth=0.1)

    ax.set_xlim(0.5,N+0.5)
    ax.set_ylim(0.5,N+0.5)
    ax.set_xticks(np.arange(10,N-0.5,10))
    ax.set_xticklabels(np.arange(10,N,10), fontsize=5)            
    ax.set_yticks(np.arange(10,N-0.5,10))
    ax.set_yticklabels(np.arange(10,N,10) , fontsize=5)  

    ax.tick_params(direction='out', width=0.2, length=2)

    for axis in ['top','bottom','left','right']:
        ax.spines[axis].set_linewidth(0.2)
    
    return fig, ax