如何绘制seaborn岭图

Question

使用seaborn 0.11，我想绘制一个seaborn ridge plot

我想在一个图中绘制磁谱数据。 所以y轴只计算图的数量，x轴使用 数据。 这是我所期待的一个例子。

这些是不同角度的光谱数据。 有没有办法在python中绘制这样的东西？ 提前致谢。

import matplotlib.pyplot as plt

data = np.loadtxt("0_deg.txt", skiprows=0, dtype=np.float128)
fig, ax = plt.subplots(figsize=(8, 6))
ax.plot(data, markersize=1, label="0° ")

数据看起来像这样

数据

269.09019   0.10781
269.09208   0.10908
269.09397   0.11928
269.09587   0.11800
269.09776   0.11418
269.09966   0.11545
269.10155   0.11928
269.10344   0.11673
269.10534   0.10781
269.10723   0.10526
269.10913   0.11418
269.11102   0.11418
269.11292   0.11291
269.11481   0.11928
269.11670   0.11928
269.11860   0.12055
269.12049   0.11928
269.12239   0.11928
269.12428   0.11673
269.12618   0.11545
269.12807   0.11545
269.12996   0.11036
269.13186   0.10908
269.13375   0.10144
269.13565   0.10908
269.13754   0.10654
269.13943   0.10399
269.14133   0.10526
269.14322   0.11418
269.14512   0.10908
269.14701   0.10272
269.14891   0.09889
269.15080   0.10526
269.15269   0.09889
269.15459   0.09635
269.15648   0.09889
269.15838   0.10017
269.16027   0.09507
269.16217   0.08998
269.16406   0.09507
269.16595   0.08870
269.16785   0.09252
269.16974   0.09762
269.17164   0.09889
269.17353   0.09507
269.17542   0.10017
269.17732   0.10399
269.17921   0.10144
269.18111   0.09762
269.18300   0.10144
269.18490   0.10144
269.18679   0.09635
269.18868   0.10017
269.19058   0.10399
269.19247   0.10017
269.19437   0.10017
269.19626   0.09889
269.19816   0.10017
269.20005   0.09507
269.20194   0.09635
269.20384   0.09380
269.20573   0.09252
269.20763   0.08998

Answer 1

岭图

• 使用pathlib和.glob查找目录中的所有文件
• 将文件加载到pandas.DataFrames list中
  • 文件名用下划线分割，并使用索引-1的值作为每组数据的'label'列值。 该值为0deg 、 10deg等。
    • 每个文件的标签必须是唯一的，因为绘图行由标签决定。
  • 给定f = WindowsPath('data/CuSo4_10mV_300mS_Amod9.44V_0deg')作为pathlib对象
    • f.suffix是'.44V_0deg'
    • f.suffix.split('_')[-1]是'0deg'
  • 添加了'label'列，以便可以为每条绘图线识别正确的'intensity'值。
• 使用pandas.concat组合数据pandas.concat列表。
• 山脊图的 DataFrame 必须采用长（整洁）格式

import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

sns.set_theme(style="white", rc={"axes.facecolor": (0, 0, 0, 0)})

# find the local files
p = Path('c:/somepathtofiles')  # p = Path.cwd()  # for data in the current working directory
files = list(p.glob('*.44V*'))

# load all the data, but create a dataframe in the correct form for a RidgePlot
dfl = list()
for f in files:
    v = pd.read_csv(f, sep='\\s+', header=None, usecols=[1])
    v.columns = ['intensity']
    v['label'] = f.suffix.split('_')[-1]
    dfl.append(v)

# combine the list of dataframes into a single dataframe    
df = pd.concat(dfl)

# plot
# Initialize the FacetGrid object
pal = sns.cubehelix_palette(len(df.label.unique()), rot=-.25, light=.7)
g = sns.FacetGrid(df, row="label", hue="label", aspect=15, height=.5, palette=pal)

# Draw the densities in a few steps
g.map(sns.kdeplot, "intensity", bw_adjust=.5, clip_on=False, fill=True, alpha=1, linewidth=1.5)
g.map(sns.kdeplot, "intensity", clip_on=False, color="w", lw=2, bw_adjust=.5)
g.map(plt.axhline, y=0, lw=2, clip_on=False)

# Define and use a simple function to label the plot in axes coordinates
def label(x, color, label):
    ax = plt.gca()
    ax.text(0, .2, label, fontweight="bold", color=color, ha="left", va="center", transform=ax.transAxes)

g.map(label, "intensity")

# Set the subplots to overlap
g.fig.subplots_adjust(hspace=-.25)

# Remove axes details that don't play well with overlap
g.set_titles("")
g.set(yticks=[])
g.despine(bottom=True, left=True)

# uncomment the following line if there's a tight layout warning
# g.fig.tight_layout()

更新前

import pandas as pd
import matplotlib.pyplot as plt
from pathlib import Path

###########################################################
# Use if loading the data from the local computer

# create the path to the files
p = Path('c:/somepathtofiles')

# if loading the data from the local computer
# get a generator of all the files
files = p.glob('*.44V*')

# load the files into a dict of pandas.DataFrames
dfd = {f'{file.suffix.split("_")[-1]}': pd.read_csv(file, sep='\\s+', header=None) for file in files}

###########################################################
# Use if loading data from GitHub

# don't use both lines for files.
files = [f'https://raw.githubusercontent.com/mahesh27dx/NPR/master/CuSo4_10mV_300mS_Amod9.44V_{v}deg' for v in range(0, 190, 10)]

# load the files into a dict of pandas.DataFrames
dfd = {f'{file.split("_")[-1]}': pd.read_csv(file, sep='\\s+', header=None) for file in files}

###########################################################

# iterate through the dict
plt.figure(figsize=(10, 8))  # set up plot figure
for k, v in dfd.items():
    dfd[k].columns = ['mag_field', 'intensity']

    sns.lineplot(x='mag_field', y='intensity', data=v, label=k)
    
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
plt.xlabel('Magnetic Field')
plt.ylabel('Field Intensity')
plt.show()