>source

같은 플롯 신호에서 후보의 튜플이 나타내는 모든 시작점과 끝점을 인쇄하고 싶습니다. 나는 아래를 시도했지만 다른 플롯을 그릴 수만 있습니다. 그러나 첫 번째 플롯(60, 822), 두 번째 플롯(829, 159) 및 세 번째(832, 346)와 같이 동일한 그림에 시작점과 끝점(ind1 및 ind2)을 표시하고 싶습니다. 데이터 신호이지만 다른 지점에 있습니다.

예상 출력의 예는 다음과 같습니다.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
def plot(ind1, ind2, filename='test.png'):
    fig= plt.figure(figsize=(20,6))
    ax= fig.add_subplot(211)
    d1= data[ind1:ind1+n]
    d2= data[ind2:ind2+n]
    ax.plot(range(ind1,ind1+n),d1, color='g', marker='', linestyle='-', lw=2., mew=2.)
    ax.plot(range(ind2,ind2+n),d2, color='b', marker='', linestyle='-', lw=2., mew=2.)
    ax.plot(range(len(data)), data, color='k', marker='', linestyle='-', alpha=.5, mew=1.)
    ax.set_xlabel('t')
    ax.set_ylabel('T')
    plt.suptitle('Title')
    fig.savefig(filename, format='png')
    plt.close()
def build_cm():
    candidates= [(60, 822), (829, 159), (832, 346)]
    for i in range(len(candidates)):
      ind1, ind2= candidates[i]
      plot(ind1, ind2, 'comp_%s_%s.png'%(ind1, ind2))
if __name__== '__main__':
    n= 40
    data= pd.read_csv('data.csv')
    build_cm()

data.csv

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606.0

세그먼트(829, 159)란 무엇입니까? 어떻게 두 번째 인덱스가 첫 번째 인덱스보다 낮을 수 있습니까? 그리고 샘플 데이터에는 700개의 데이터 포인트가 있습니다. 여기서 인덱스 829는 무엇을 의미합니까?

Mr. T2022-02-08 04:27:11

그러면 세그먼트 822, 829 및 832가 서로 겹치지 않습니까? 어느 것이 어느 것인지 어떻게 알 수 있습니까? 무엇을 하려는 것인지 정확히 이해하기 어렵습니다. 플롯 대신 build_cm에서 Figure를 빌드, 인쇄 및 닫는 것이 어떻게 목표를 달성하지 못합니까?

wikikikitiki2022-02-08 04:27:11

일부는 겹칠 수 있지만 적어도 지금은 괜찮습니다. build_cm에서 그렇게 할 수 있다면 다른 곳에서도 할 수 있다고 확신합니다. 따라서 build_cm에서 그렇게 할 수 있는 솔루션도 환영합니다. 원래 신호에서 다른 [start:start+40] 세그먼트를 강조 표시하는 솔루션을 찾기 위해 고군분투하고 있습니다.

Gee2022-02-08 04:27:11
  • 답변 # 1

    두 번째 접근 방식
    설명했듯이 원래 추적에 세그먼트를 표시하려고 합니다. 색상 범위를 정의하고 동일한 트레이스에 각각을 표시할 수 있습니다. 그러나 귀하의 세그먼트가 겹칠 수 있으므로 동일한 플롯에 표시하지 않습니다. 대신 서브플롯에 배포하고 다르게 색칠할 수 있습니다.

    import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt
    def plot(data, indexes, n= 40, filename='test.png'):
        data= data.values.flat
        fig, axes= plt.subplots(len(indexes), figsize=(10, 15), sharex=True, sharey=True)
        vals_idx= np.arange(len(data))
        mask= np.ones_like(vals_idx, dtype=bool)
        for curr_idx, curr_ax in zip(indexes, axes):
            curr_ax.plot(vals_idx, data, color="grey", marker='', linestyle='-', lw=2., mew=2., alpha=0.5)
            mask0, mask1= mask.copy(), mask.copy()
            mask0[curr_idx[0]:curr_idx[0]+n]= False
            curr_ax.plot(np.ma.masked_where(mask0, vals_idx), np.ma.masked_where(mask0, data), color='b', marker='', linestyle='-', lw=2., mew=2., label=f"first: {curr_idx[0]}")
            mask1[curr_idx[1]:curr_idx[1]+n]= False
            curr_ax.plot(np.ma.masked_where(mask1, vals_idx), np.ma.masked_where(mask1, data), color='red', marker='', linestyle='-', lw=2., mew=2., label=f"second: {curr_idx[1]}")
            curr_ax.legend()
            curr_ax.set_ylabel('T')
        curr_ax.set_xlabel('t')
        fig.suptitle('Title')
        fig.subplots_adjust(top=0.95)
        fig.savefig(filename, format='png')
        plt.show()
    if __name__== '__main__':
        n= 40
        data= pd.read_csv("test.txt")
        candidates= [(60, 822), (829, 159), (832, 346)]
        output_name= "test.png"
        plot(data, candidates, n, output_name)
    

    샘플 출력:

    첫 번째 접근 방식
    추적 세그먼트가 다른 부분에서 수집되기 때문에 샘플 출력의 연속 데이터가 어디에서 왔는지 모르겠습니다. 그러나 마스크 배열을 사용하여 이 문제에 접근합니다.

    import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt
    def plot(data, indexes, n= 40, filename='test.png'):
        fig, ax= plt.subplots(figsize=(20,6))
        idx= np.asarray(indexes)
        mask=  np.tile(np.concatenate([n * [True], n * [False]]), idx.shape[0])
        vals= np.asarray([data[i:i+n] for i in idx.flat])
        vals_idx= np.arange(vals.size)
        ax.plot(np.ma.masked_where(mask, vals_idx), np.ma.masked_where(mask, vals.flat), color='g', marker='', linestyle='-', lw=2., mew=2.)
        ax.plot(np.ma.masked_where(~mask, vals_idx), np.ma.masked_where(~mask, vals.flat), color='b', marker='', linestyle='-', lw=2., mew=2.)
        ax.set_xlabel('t')
        ax.set_ylabel('T')
        plt.suptitle('Title')
        #fig.savefig(filename, format='png')
        #plt.close()
        plt.show()
    if __name__== '__main__':
        n= 40
        data= pd.read_csv("test.csv")
        candidates= [(60, 822), (829, 159), (832, 346)]
        output_name= "test.png"
        plot(data, candidates, n, output_name)
    

    샘플 출력:

    정말 감사합니다. 좋은 시작이라고 생각합니다. 그러나 전체 원본 신호를 유지하고 신호에 표시하거나 강조 표시하고 싶습니다. 특정 세그먼트는 인덱스 [start:start+40]로 표시됩니다. 원래 신호를 끊거나 변경하지 않고 신호의 특정 간격에 색상을 지정하는 것과 거의 같습니다. 원래 신호를 유지하는 것이 중요한 이유는 원래 신호와 관련하여 각 세그먼트의 실제 시작 및 끝 위치를 시각화해야 하기 때문입니다. 당신이 제안한 솔루션은 플롯에서 이 시각적 정보를 잃게 됩니다(그러나 매우 좋은 시작임).

    Gee2022-02-08 04:27:11

    업데이트를 참조하십시오. 왜 후보자들이 짝을 이루는지 아직도 이해가 되지 않습니다. 의도한 비교의 구조가 불분명합니다. 그리고 wikikitiki가 이미 말했듯이 동일한 추적에서 겹치는 부분을 표시할 수 없습니다.

    Mr. T2022-02-07 06:03:03
  • 이전 R에서 비선형 방정식 시스템의 엄격하게 양의 솔루션을 찾는 방법은 무엇입니까?
  • 다음 `docker-compose up`은 PostgreSQL 컨테이너에 대한 영구 볼륨을 생성하지 않습니다.