Python 常用命令

papers_dates[(papers_dates.preprint_test == True) & ~ papers_dates.acptDate.isna()]
# 两列做 or 运算
df_all_norm[(df_all_norm["CO2"] > 0.8) | (df_all_norm["CO"] > 0.8)]

papers_age[papers_age.pubDate >= "2028-03-01"]

papers_dates[papers_dates.preprint_age > pd.Timedelta(30,'D')]

df.dropna(subset=['name', 'toy'])

pd.concat([papers, CSCoV_scores], axis=1)

papers_redup.merge(all_rxiv_redup[['subDate', 'title']], left_on='title', right_on='title',how='left')

df.replace([0, 1, 2, 3], [4, 3, 2, 1])

pd.set_option('display.max_colwidth', None)

X_lxd[X_lxd.title.isin(selected)][['title', 'pub_prob']]

papers_dates[['subDate', 'acptDate', 'pubDate']].describe(datetime_is_numeric=True)

X_lxd.sort_values(by='pub_prob', ascending=False)

x = np.random.rand(3,1)
1 + x # element-wise operation
1 - x
1 * x
1 / x

x = np.zeros((10, 1))
x = np.ones((10, 1))

x = np.random.rand(3,1)
x.mean()
x.std()
x.max()
x.min()
np.median(x)

x = np.random.rand(3,1)
y = np.random.rand(3,1)
np.maximum(x, y) # also np.maximum(x, 0) broadcast by default
# Out: array([[0.88240057], [0.38776741], [0.72610557]])

x = np.random.rand(3,1)
y = np.random.rand(3,1)
x * y # element-wise multiplication, return vector (3,1), or np.multiply()
np.transpose(x).dot(y) # return scalar
x / y # element-wise division, return vector (3,1)

np.absolute(np.random.rand(3, 2，2)-10)

x = np.random.rand(3, 2，2)
x.shape   # (3, 2，2)
x.reshape(-1, 1) # a column vector, 1 f

# reshape a np.array of images
train_set_x_flatten = train_set_x_orig.reshape(train_set_x_orig.shape[0], -1).T

truncnorm.rvs(a, b, size=1000)

import multiprocessing

def f(x):
    return x*x

if __name__ == '__main__':
 
    pool = multiprocessing.Pool(processes=2)
    numList = [i for i in range(10)]
    sub_procs = []
 
    for num in numList:
        proc = pool.apply_async(f, (num,))
        sub_procs.append(proc)
    pool.close()
    pool.join()
 
    result = [proc.get() for proc in sub_procs]
    print(result)

def plot_kmer_signals(k_mer):
    dup_list = df_read_kmers_not_nan_dup[df_read_kmers_not_nan_dup.kmer == k_mer].index.to_list()
    plt.figure(figsize=(20, 8))
    if len(dup_list) > 4: dup_list = dup_list[:4]
    sig_lens = []
    for i, idx in enumerate(dup_list):
        plt.subplot(len(dup_list), 1, i+1)
        plt.plot(signals[idx])
        plt.xlim(0, 3000)
        plt.ylim(0, 200)
        sig_lens.append(len(signals[idx]))
    plt.show()
    print(sig_lens)

def plot_step_curve(ax, prop, ylabel=None, yrefs=[]):
    ylabel = prop if ylabel is None else ylabel
    
    for yc in yrefs:
        ax.axhline(y=yc, ls=':', label=f"y={yc:4.2f}")
    df_r = reinvent_step_n[reinvent_step_n.Validity == True][['step', prop]].groupby('step').mean()
    df_s = sgpt_step_n[sgpt_step_n.Validity == True][['step', prop]].groupby('step').mean()
    
    ax.plot(df_r.index.values, df_r[prop].values, label="Reinvent")
    ax.plot(df_s.index.values, df_s[prop].values, label="SGPT-RL")
    ax.set_xlabel('Step')
    ax.set_ylabel(ylabel)
    ax.legend()
    
fig, axs = plt.subplots(2, 2, figsize=(16, 9))

plot_step_valid_curve(axs[0,0], 'Validity', ylabel='Validity')
axs[0,0].text(-0.1, 1.05, "a", transform=axs[0,0].transAxes, size=16, weight='bold')
plot_step_curve(axs[0,1], 'Activity', ylabel='DRD2 activity')
axs[0,1].text(-0.1, 1.05, "b", transform=axs[0,1].transAxes, size=16, weight='bold')
plot_step_ring_count_curve(axs[1,0], step_lim=1000)
axs[1,0].text(-0.07, 1.05, "c", transform=axs[1,0].transAxes, size=16, weight='bold')
plot_step_new_scaffold_curve(axs[1,1])
axs[1,1].text(-0.1, 1.05, "d", transform=axs[1,1].transAxes, size=16, weight='bold')

fig.savefig("figures/drd2_step.pdf", bbox_inches='tight')
fig.show()

plt.savefig(save_path, dpi=300, bbox_inches='tight')

from easydict import EasyDict

my_dict = EasyDict()
my_dict.root_path = os.path.dirname(config_file)

my_dict.root_path  # like a property in the object