首页fig = px.scatter(data_forecast_is_stk_bs_rpt_lishichayi_qukong_zq_qc,x=data_forecast_is_stk_bs_rpt_lishichayi_qukong_zq_qc.index, y="归母利润历史年差异" ) fig = px.scatter(data_forecast_is_stk_bs_rpt_lishichayi_qukong_cw_qc,x=data_forecast_is_stk_bs_rpt_lishichayi_qukong_cw_qc.index, y="归母利润历史年差异" ) fig.show() 上面的数据散点是蓝色下面的数据散点是橙色

fig = px.scatter(data_forecast_is_stk_bs_rpt_lishichayi_qukong_zq_qc,x=data_forecast_is_stk_bs_rpt_lishichayi_qukong_zq_qc.index, y="归母利润历史年差异" ) fig = px.scatter(data_forecast_is_stk_bs_rpt_lishichayi_qukong_cw_qc,x=data_forecast_is_stk_bs_rpt_lishichayi_qukong_cw_qc.index, y="归母利润历史年差异" ) fig.show() 上面的数据散点是蓝色下面的数据散点是橙色

时间: 2023-07-31 18:10:38 浏览: 91

colourhistogram.rar_CBIR.fig_DWT.fig_cbir_cbir dwt_feature band

Colourhistogram II. TEXTURE FEATURE EXTRACTION IN CBIR An overview of the proposed CBIR system is illustrated in Fig. 1. The proposed algorithm, Label Wavelet Transform (LWT), is based on color image segmentation [1], and it is an extension of DWT-based texture feature extraction method. The 2-D DWT is computed by applying separable filter banks to the gray level images. The detail images Dn,1, Dn,2, and Dn,3 are obtained by band-pass filtering in a specific direction, and they can be categorized into three frequency bands: HL, LH, HH band, respectively. Each band contains different directional information at scale n. The texture feature is extracted from the variance (ó2 n,i) of the coefficients cn,i of the detail image Dn,1, Dn,2, and Dn,3 at different scale n.To represent the texture feature of an image q, the texture feature vector of DWT is defined as [2]: TDWT (q) = [ó2 1,1, ó2 1,2, ó2 1,3, ..., ó2N max,3], (1) where Nmax denotes the largest scale. In this work, Nmax

您可以在绘制两个散点图时，分别使用不同的颜色。例如，将上面的散点图设置为蓝色，下面的散点图设置为橙色，可以使用以下代码： ```python fig1 = px.scatter(data_forecast_is_stk_bs_rpt_lishichayi_qukong_zq_qc, x=data_forecast_is_stk_bs_rpt_lishichayi_qukong_zq_qc.index, y="归母利润历史年差异", color_discrete_sequence=["blue"]) fig2 = px.scatter(data_forecast_is_stk_bs_rpt_lishichayi_qukong_cw_qc, x=data_forecast_is_stk_bs_rpt_lishichayi_qukong_cw_qc.index, y="归母利润历史年差异", color_discrete_sequence=["orange"]) fig1.show() fig2.show() ``` 这样，第一个散点图的颜色就会被设置成蓝色，第二个散点图的颜色会被设置成橙色。如果您需要使用其他颜色，只需要将 `color_discrete_sequence` 参数的值改为对应颜色的名称或 RGB 值即可。

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