added plot_on_terminal.py

examples/002_example/hamish.py

+import tree_maker, json
+import pandas as pd
+from rich.progress import track, Progress
+import termplotlib as tpl
+import numpy as np
+import plotext as plt
+
+
+def read_json(file_name):
+    """
+    Reads a json file and returns it in python.
+    """
+    with open(file_name) as json_file:
+        json_object = json.load(json_file)
+    return json_object
+
+
+def plot_df(df, set_minimum_time=True):
+    """
+        Plots the columns of a dataframe. Either all of the columns in the dataframne can be plotted at once, or specific columns can be plotted seperately. For the example we will look at a datafraame with four columns: mutated, submitted, started and completed.
+
+    Examples
+    --------
+    >>>plot_df(my_dataframe)
+
+    This will return a plot of all four columns.
+
+    >>>plot_df(my_dataframe['completed'])
+
+    This will return a plot of only the column 'completed'.
+    """
+    if set_minimum_time == True:
+        df = (df - df.min().min()) / 1e9
+    for my_column in df.columns:
+        y = df[my_column]
+        plt.scatter(y)
+        plt.plotsize(100, 30)
+        plt.title(f"{my_column}")
+        plt.xlabel("number of jobs")
+        plt.ylabel("time (s)")
+    plt.show()
+
+
+def return_dataframe(node_list):
+    """
+    Returns a formatted pandas dataframe.
+    """
+    my_list = []
+    for my_node in node_list:
+        my_list.append(read_json(my_node.log_file))
+    return pd.DataFrame(my_list)
+
+
+def add_duration(df):
+    """
+    Adds a duration column to the dataframe, which is the time a job takes to be completed after it is started.
+    """
+    df["duration"] = df["completed"] - df["started"]
+
+
+def add_pending(df):
+    """
+    Adds a pending column to the dataframe, which is the time a job takes to start after it has been submitted.
+    """
+    df["pending"] = df["started"] - df["submitted"]
+
+
+def show_progress(df):
+    """
+    Shows the progress of the jobs which are running. It returns a set of progress bars corresponding to the status of the different jobs. So it would return progress bars for 'mutated', submitted' etc.
+    """
+    with Progress() as progress:
+        tasks = []
+        for my_column in df.columns:
+            tasks.append(
+                progress.add_task(
+                    f"{my_column}...{len(df[my_column].dropna())}/{len(df)}",
+                    total=len(df),
+                )
+            )
+
+        for my_index, my_column in enumerate(df.columns):
+            progress.update(tasks[my_index], completed=len(df[f"{my_column}"].dropna()))
+
+
+if __name__ == "__main__":
+    root = tree_maker.tree_from_json(f"./study_000/tree.json")
+    my_df = return_dataframe(root.generation(1))
+    plot_all(my_df)

tree_maker/plot_on_terminal.py

+import tree_maker, json
+import pandas as pd
+from rich.progress import track, Progress
+import termplotlib as tpl
+import numpy as np
+import plotext as plt
+
+
+def read_json(file_name):
+    """
+    Reads a json file and returns it in python.
+    """
+    with open(file_name) as json_file:
+        json_object = json.load(json_file)
+    return json_object
+
+
+def plot_df(df, set_minimum_time=True):
+    """
+        Plots the columns of a dataframe. Either all of the columns in the dataframne can be plotted at once, or specific columns can be plotted seperately. For the example we will look at a datafraame with four columns: mutated, submitted, started and completed.
+
+    Examples
+    --------
+    >>>plot_df(my_dataframe)
+
+    This will return a plot of all four columns.
+
+    >>>plot_df(my_dataframe['completed'])
+
+    This will return a plot of only the column 'completed'.
+    """
+    if set_minimum_time == True:
+        df = (df - df.min().min()) / 1e9
+    for my_column in df.columns:
+        y = df[my_column]
+        plt.scatter(y)
+        plt.plotsize(100, 30)
+        plt.title(f"{my_column}")
+        plt.xlabel("number of jobs")
+        plt.ylabel("time (s)")
+    plt.show()
+
+
+def return_dataframe(node_list):
+    """
+    Returns a formatted pandas dataframe.
+    """
+    my_list = []
+    for my_node in node_list:
+        my_list.append(read_json(my_node.log_file))
+    return pd.DataFrame(my_list)
+
+
+def add_duration(df):
+    """
+    Adds a duration column to the dataframe, which is the time a job takes to be completed after it is started.
+    """
+    df["duration"] = df["completed"] - df["started"]
+
+
+def add_pending(df):
+    """
+    Adds a pending column to the dataframe, which is the time a job takes to start after it has been submitted.
+    """
+    df["pending"] = df["started"] - df["submitted"]
+
+
+def show_progress(df):
+    """
+    Shows the progress of the jobs which are running. It returns a set of progress bars corresponding to the status of the different jobs. So it would return progress bars for 'mutated', submitted' etc.
+    """
+    with Progress() as progress:
+        tasks = []
+        for my_column in df.columns:
+            tasks.append(
+                progress.add_task(
+                    f"{my_column}...{len(df[my_column].dropna())}/{len(df)}",
+                    total=len(df),
+                )
+            )
+
+        for my_index, my_column in enumerate(df.columns):
+            progress.update(tasks[my_index], completed=len(df[f"{my_column}"].dropna()))
+
+
+if __name__ == "__main__":
+    root = tree_maker.tree_from_json(f"./study_000/tree.json")
+    my_df = return_dataframe(root.generation(1))
+    plot_all(my_df)