Add Python3 port and example plotting script

Add a Python 3 port of the CREST model as pyCREST_Py_3.py and an example script Example_Py_3.py that generates profiles and provides helper plotting functions (plot_powerfile, plot_occupancy_file, plot_app_profiles). Rename the original pyCREST.py to pyCREST_Py_2.py to preserve the prior copy. The example uses pyCREST_Py_3.create_profiles to produce Pfile_/Qfile_/Occfile_/AppProfiles files and attempts to display or save plots (matplotlib + numpy).

Author: programmasters

Example_Py_3.py

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+import pyCREST_Py_3 as cr
+import numpy as np
+import matplotlib.pyplot as plt
+
+def plot_powerfile(filename):
+    """Read a power output file created by create_profiles and plot it.
+
+    The files generated by :func:`create_profiles` are named ``Pfile_<idstring>.dat``
+    where each row corresponds to one dwelling and contains 1440 minute values.
+    This helper loads the file and displays the first profile as a simple line plot.
+    """
+    # load the data (assumes integer or float values separated by whitespace)
+    data = np.loadtxt(filename)
+    if data.ndim == 1:
+        profile = data
+    else:
+        profile = data[0]
+
+    times = np.arange(len(profile)) / 60.0  # hours
+    plt.figure()
+    plt.plot(times, profile)
+    plt.xlabel('Time (hours)')
+    plt.ylabel('Active power demand')
+    plt.title(f'Profile from {filename}')
+    plt.grid(True)
+    plt.show()
+
+
+
+def plot_occupancy_file(filename):
+    """Plot an occupancy profile generated by :func:`create_profiles`.
+
+    The occupancy files are created as ``Occfile_<idstring>.dat`` and contain
+    144 ten-minute values for each dwelling representing the occupancy state.
+    """
+    data = np.loadtxt(filename)
+    # there may be a single row (1D) or multiple rows; use first profile
+    if data.ndim == 1:
+        profile = data
+    else:
+        profile = data[0]
+    times = np.arange(len(profile)) * 10.0 / 60.0  # hours
+    plt.figure()
+    plt.plot(times, profile)
+    plt.xlabel('Time (hours)')
+    plt.ylabel('Occupancy state')
+    plt.title(f'Occupancy from {filename}')
+    plt.grid(True)
+    plt.show()
+
+
+def plot_app_profiles(filename, appliance=None):
+    """Plot one appliance's power profile from an AppProfiles file.
+
+    Each line of ``AppProfiles<idstring>.dat`` normally contains:
+    ``<index> <P/Q> <name> <1440 values>``.  The parser looks for the named
+    appliance token and then interprets subsequent tokens as floats.  If no
+    *appliance* is specified, the first numeric profile encountered is used.
+    """
+    lines = open(filename).read().strip().splitlines()
+    selected = None
+    for line in lines:
+        tokens = line.split()
+        if not tokens:
+            continue
+        # find the token that equals the requested appliance (or just the first name)
+        if appliance is not None and appliance in tokens:
+            idx = tokens.index(appliance)
+        elif appliance is None and len(tokens) >= 3:
+            # assume third token is name
+            idx = 2
+        else:
+            continue
+        # collect floats after the name token
+        vals = []
+        for tok in tokens[idx+1:]:
+            try:
+                vals.append(float(tok))
+            except ValueError:
+                # stop at first non‑numeric (there may not be any)
+                break
+        if vals:
+            name = tokens[idx]
+            selected = (name, np.array(vals))
+            break
+    if selected is None:
+        print(f'no appliance profile found in {filename}')
+        return
+    name, profile = selected
+    times = np.arange(len(profile)) / 60.0  # hours
+    plt.figure()
+    plt.plot(times, profile)
+    plt.xlabel('Time (hours)')
+    plt.ylabel(f'{name} power')
+    plt.title(f'{name} profile from {filename}')
+    plt.grid(True)
+    plt.show()
+
+
+if __name__ == "__main__":
+    # generate profiles and plot the active power demand
+    n=1
+    month=1
+    daytype="weekday"
+    cr.create_profiles(n=n,daytype=daytype,month=month)
+
+    # construct filename matching create_profiles output and show it
+    idstring = f"{n}x_month-{month}_daytype-{daytype}"
+    power_filename = f"Pfile_{idstring}.dat"
+    print(f"generated {power_filename}")
+    # display the plot automatically (will open a window or save a PNG)
+    try:
+        plot_powerfile(power_filename)
+    except Exception as e:
+        print(f"plot failed: {e}, saving figure instead")
+        # regenerate profile data and save to file
+        import matplotlib.pyplot as _plt
+        data = np.loadtxt(power_filename)
+        times = np.arange(len(data)) / 60.0
+        _plt.figure()
+        _plt.plot(times, data if data.ndim==1 else data[0])
+        _plt.savefig(power_filename.replace('.dat', '.png'))
+        print(f"saved plot to {power_filename.replace('.dat', '.png')}")
+
+    # also plot occupancy and one appliance profile
+    occ_filename = f"Occfile_{idstring}.dat"
+    try:
+        plot_occupancy_file(occ_filename)
+    except Exception as e:
+        print(f"occupancy plot failed: {e}")
+    app_filename = f"AppProfiles{idstring}.dat"
+    try:
+        plot_app_profiles(app_filename, appliance='LIGHTING')
+    except Exception as e:
+        print(f"appliance plot failed: {e}")
+