hyperactive.dashboard.run_streamlit.main() - Code Metrics - Inspection of "rename methods" - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Push — master ( cba5ed...7e35c2 )

by Simon

created 2021-06-17 05:19 UTC

hyperactive.dashboard.run_streamlit.main() C

↳ Parent: hyperactive.dashboard.run_streamlit

Complexity

Conditions

Size

Total Lines	76
Code Lines	53

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	9
eloc	53
nop	0
dl	0
loc	76
rs	6.2048
c	0
b	0
f	0

How to fix Long Method

# Author: Simon Blanke
# Email: [email protected]
# License: MIT License

import os
import sys
import time
import numpy as np
import pandas as pd
import streamlit as st
import plotly.express as px
import matplotlib.pyplot as plt


color_scale = px.colors.sequential.Jet


def parallel_coordinates_plotly(*args, plotly_width=1200, plotly_height=540, **kwargs):
    fig = px.parallel_coordinates(*args, **kwargs, color_continuous_scale=color_scale)
    fig.update_layout(autosize=False, width=plotly_width, height=plotly_height)

    return fig


def filter_data(filter, df, columns):
    if len(df) > 1:
        for column in columns:
            if column not in list(filter["parameter"]):
                continue

            filter_ = filter[filter["parameter"] == column]
            lower, upper = (
                filter_["lower bound"].values[0],
                filter_["upper bound"].values[0],
            )

            col_data = df[column]

            if lower == "lower":
                lower = np.min(col_data)
            else:
                lower = float(lower)

            if upper == "upper":
                upper = np.max(col_data)
            else:
                upper = float(upper)

            df = df[(df[column] >= lower) & (df[column] <= upper)]

    return df


def main():
    try:
        st.set_page_config(page_title="Hyperactive Progress Board", layout="wide")
    except:
        pass

    search_ids = sys.argv[1:]

    search_id_dict = {}
    for search_id in search_ids:
        search_id_dict[search_id] = {}

        progress_data_path = "./progress_data_" + search_id + ".csv~"
        filter_path = "./filter_" + search_id + ".csv"

        if os.path.isfile(progress_data_path):
            search_id_dict[search_id]["progress_data"] = pd.read_csv(progress_data_path)
        if os.path.isfile(filter_path):
            search_id_dict[search_id]["filter"] = pd.read_csv(filter_path)

    for search_id in search_id_dict.keys():
        progress_data = search_id_dict[search_id]["progress_data"]
        filter = search_id_dict[search_id]["filter"]

        st.title(search_id)
        st.components.v1.html(
            """<hr style="height:1px;border:none;color:#333;background-color:#333;" /> """,
            height=10,
        )

        col1, col2 = st.beta_columns([1, 2])

        progress_data_f = progress_data[
            ~progress_data.isin([np.nan, np.inf, -np.inf]).any(1)
        ]

        nth_iter = progress_data_f["nth_iter"]
        score_best = progress_data_f["score_best"]
        nth_process = list(progress_data_f["nth_process"])

        if np.all(nth_process == nth_process[0]):
            fig, ax = plt.subplots()
            plt.plot(nth_iter, score_best)
            col1.pyplot(fig)
        else:
            fig, ax = plt.subplots()
            ax.set_xlabel("nth iteration")
            ax.set_ylabel("score")

            for i in np.unique(nth_process):
                nth_iter_p = nth_iter[nth_process == i]
                score_best_p = score_best[nth_process == i]
                plt.plot(nth_iter_p, score_best_p, label=str(i) + ". process")
            plt.legend()
            col1.pyplot(fig)

        progress_data_f.drop(
            ["nth_iter", "score_best", "nth_process"], axis=1, inplace=True
        )
        prog_data_columns = list(progress_data_f.columns)

        progress_data_f = filter_data(filter, progress_data_f, prog_data_columns)

        # remove score
        prog_data_columns.remove("score")

        fig = parallel_coordinates_plotly(
            progress_data_f, dimensions=prog_data_columns, color="score"
        )
        col2.plotly_chart(fig)

        for _ in range(3):
            st.write(" ")

    time.sleep(1)
    st.experimental_rerun()


if __name__ == "__main__":
    main()


1			# Author: Simon Blanke
2			# Email: [email protected]
3			# License: MIT License
4
5			import os
6			import sys
7			import time
8			import numpy as np
9			import pandas as pd
10			import streamlit as st
11			import plotly.express as px
12			import matplotlib.pyplot as plt
13
14
15			color_scale = px.colors.sequential.Jet
16
17
18			def parallel_coordinates_plotly(args, plotly_width=1200, plotly_height=540, *kwargs):
19			fig = px.parallel_coordinates(args, *kwargs, color_continuous_scale=color_scale)
20			fig.update_layout(autosize=False, width=plotly_width, height=plotly_height)
21
22			return fig
23
24
25			def filter_data(filter, df, columns):
26			if len(df) > 1:
27			for column in columns:
28			if column not in list(filter["parameter"]):
29			continue
30
31			filter_ = filter[filter["parameter"] == column]
32			lower, upper = (
33			filter_["lower bound"].values[0],
34			filter_["upper bound"].values[0],
35			)
36
37			col_data = df[column]
38
39			if lower == "lower":
40			lower = np.min(col_data)
41			else:
42			lower = float(lower)
43
44			if upper == "upper":
45			upper = np.max(col_data)
46			else:
47			upper = float(upper)
48
49			df = df[(df[column] >= lower) & (df[column] <= upper)]
50
51			return df
52
53
54			def main():
55			try:
56			st.set_page_config(page_title="Hyperactive Progress Board", layout="wide")
57			except:
58			pass
59
60			search_ids = sys.argv[1:]
61
62			search_id_dict = {}
63			for search_id in search_ids:
64			search_id_dict[search_id] = {}
65
66			progress_data_path = "./progress_data_" + search_id + ".csv~"
67			filter_path = "./filter_" + search_id + ".csv"
68
69			if os.path.isfile(progress_data_path):
70			search_id_dict[search_id]["progress_data"] = pd.read_csv(progress_data_path)
71			if os.path.isfile(filter_path):
72			search_id_dict[search_id]["filter"] = pd.read_csv(filter_path)
73
74			for search_id in search_id_dict.keys():
75			progress_data = search_id_dict[search_id]["progress_data"]
76			filter = search_id_dict[search_id]["filter"]
77
78			st.title(search_id)
79			st.components.v1.html(
80			"""<hr style="height:1px;border:none;color:#333;background-color:#333;" /> """,
81			height=10,
82			)
83
84			col1, col2 = st.beta_columns([1, 2])
85
86			progress_data_f = progress_data[
87			~progress_data.isin([np.nan, np.inf, -np.inf]).any(1)
88			]
89
90			nth_iter = progress_data_f["nth_iter"]
91			score_best = progress_data_f["score_best"]
92			nth_process = list(progress_data_f["nth_process"])
93
94			if np.all(nth_process == nth_process[0]):
95			fig, ax = plt.subplots()
96			plt.plot(nth_iter, score_best)
97			col1.pyplot(fig)
98			else:
99			fig, ax = plt.subplots()
100			ax.set_xlabel("nth iteration")
101			ax.set_ylabel("score")
102
103			for i in np.unique(nth_process):
104			nth_iter_p = nth_iter[nth_process == i]
105			score_best_p = score_best[nth_process == i]
106			plt.plot(nth_iter_p, score_best_p, label=str(i) + ". process")
107			plt.legend()
108			col1.pyplot(fig)
109
110			progress_data_f.drop(
111			["nth_iter", "score_best", "nth_process"], axis=1, inplace=True
112			)
113			prog_data_columns = list(progress_data_f.columns)
114
115			progress_data_f = filter_data(filter, progress_data_f, prog_data_columns)
116
117			# remove score
118			prog_data_columns.remove("score")
119
120			fig = parallel_coordinates_plotly(
121			progress_data_f, dimensions=prog_data_columns, color="score"
122			)
123			col2.plotly_chart(fig)
124
125			for _ in range(3):
126			st.write(" ")
127
128			time.sleep(1)
129			st.experimental_rerun()
130
131
132			if __name__ == "__main__":
133			main()
134

SimonBlanke / Hyperactive

Push — master ( cba5ed...7e35c2 )

hyperactive.dashboard.run_streamlit.main() C

Complexity

Size

Duplication

Importance

How to fix Long Method

Long Method

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