Merge pull request #142 from RAMP-project/review/deprecated_appliance

Remove usage of deprecated User.Appliance class
RAMP-project · May 14, 2024 · 6b6d578 · 6b6d578
2 parents 6c1cc8c + 7ad9f39
commit 6b6d578
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diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml
@@ -26,7 +26,7 @@ jobs:
     - name: Install black
       run: |
         python -m pip install --upgrade pip
-        pip install black
+        pip install black[jupyter]==24.4.2
     - name: Lint with black
       run: |
         black . --check

diff --git a/docs/notebooks/cooking_app.ipynb b/docs/notebooks/cooking_app.ipynb
@@ -93,20 +93,20 @@
    "outputs": [],
    "source": [
     "# soup for lunch\n",
-    "lunch_window = [12 * 60, 12 * 60+26]\n",
+    "lunch_window = [12 * 60, 12 * 60 + 26]\n",
     "\n",
     "soup_1 = user_1.add_appliance(\n",
     "    name=\"soup for lunch\",\n",
-    "    power=1200, # nominal power of appliance\n",
-    "    func_time=25, # the cooking time\n",
-    "    func_cycle=25, # we always need  25 minute for cooking\n",
-    "    fixed_cycle=1, # the cookstove is not a continus power usage appliance, it has cycles as mentioned earlier \n",
-    "    window_1=lunch_window, # lunch is always prepared from 12\n",
+    "    power=1200,  # nominal power of appliance\n",
+    "    func_time=25,  # the cooking time\n",
+    "    func_cycle=25,  # we always need  25 minute for cooking\n",
+    "    fixed_cycle=1,  # the cookstove is not a continus power usage appliance, it has cycles as mentioned earlier\n",
+    "    window_1=lunch_window,  # lunch is always prepared from 12\n",
     "    p_11=1200,  # power of the first cycle\n",
     "    t_11=5,  # time needed for the first cycle\n",
     "    p_12=750,  # power of the second cycle\n",
     "    t_12=20,  # time needed for the second cycle\n",
-    "    cw11=lunch_window, # the time window of the working cycle \n",
+    "    cw11=lunch_window,  # the time window of the working cycle\n",
     ")"
    ]
   },
@@ -194,10 +194,9 @@
     "number_of_days = 5\n",
     "user_1_profiles = {}\n",
     "user_2_profiles = {}\n",
-    "for day in range(1,number_of_days+1):\n",
+    "for day in range(1, number_of_days + 1):\n",
     "    user_1_profiles[f\"day {day}\"] = pd.Series(user_1.generate_single_load_profile())\n",
-    "    user_2_profiles[f\"day {day}\"] = pd.Series(user_2.generate_single_load_profile())\n",
-    "    "
+    "    user_2_profiles[f\"day {day}\"] = pd.Series(user_2.generate_single_load_profile())"
    ]
   },
   {
@@ -218,17 +217,21 @@
     }
    ],
    "source": [
-    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 4)) \n",
+    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 4))\n",
     "\n",
     "\n",
     "i = 0\n",
     "for name, df in dict(\n",
-    "    user_1_profiles=pd.concat(user_1_profiles,axis=1).iloc[lunch_window[0]-5:lunch_window[1]+5], # take only the lunch window\n",
-    "    user_2_profiles=pd.concat(user_2_profiles,axis=1).iloc[lunch_window[0]-5:lunch_window[1]+5]# take only the lunch window\n",
-    "    ).items():\n",
-    "    df.plot(ax=axes[i],legend=True)\n",
+    "    user_1_profiles=pd.concat(user_1_profiles, axis=1).iloc[\n",
+    "        lunch_window[0] - 5 : lunch_window[1] + 5\n",
+    "    ],  # take only the lunch window\n",
+    "    user_2_profiles=pd.concat(user_2_profiles, axis=1).iloc[\n",
+    "        lunch_window[0] - 5 : lunch_window[1] + 5\n",
+    "    ],  # take only the lunch window\n",
+    ").items():\n",
+    "    df.plot(ax=axes[i], legend=True)\n",
     "    axes[i].set_title(name)\n",
-    "    i+=1\n",
+    "    i += 1\n",
     "\n",
     "plt.tight_layout()\n",
     "plt.show()"

diff --git a/docs/notebooks/fixed_flat_app.ipynb b/docs/notebooks/fixed_flat_app.ipynb
@@ -69,8 +69,8 @@
     "    func_cycle=60,\n",
     "    fixed=\"yes\",  # This means all the 'n' appliances of this kind are always switched-on together\n",
     "    flat=\"yes\",  # This means the appliance is not subject to random variability in terms of total usage time\n",
-    "    window_1 = [1200, 1440],  # from 20:00 to 24:00\n",
-    ")\n"
+    "    window_1=[1200, 1440],  # from 20:00 to 24:00\n",
+    ")"
    ]
   },
   {
@@ -109,14 +109,12 @@
    ],
    "source": [
     "school_case = UseCase(\n",
-    "    users=[school], # users of the usecase\n",
-    "    date_start=\"2023-01-01\", # start date\n",
-    "    )\n",
+    "    users=[school],  # users of the usecase\n",
+    "    date_start=\"2023-01-01\",  # start date\n",
+    ")\n",
     "\n",
     "# when both date_start and date_end are not given, you need to initialize the usecase by this method and by passing the number of days as num_days\n",
-    "school_case.initialize(\n",
-    "    num_days=7\n",
-    "    )"
+    "school_case.initialize(num_days=7)"
    ]
   },
   {
@@ -221,12 +219,11 @@
     }
    ],
    "source": [
+    "fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(12, 4))\n",
     "\n",
-    "fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(12, 4))  \n",
     "\n",
-    "\n",
-    "for i, df in enumerate([no_fix_no_flat,fix_no_flat,fixed_flat,no_fixed_flat]):\n",
-    "    df.plot(ax=axes[i],legend=False)\n",
+    "for i, df in enumerate([no_fix_no_flat, fix_no_flat, fixed_flat, no_fixed_flat]):\n",
+    "    df.plot(ax=axes[i], legend=False)\n",
     "    axes[i].set_title(df.columns[0])\n",
     "\n",
     "\n",

diff --git a/docs/notebooks/multi_cycle.ipynb b/docs/notebooks/multi_cycle.ipynb
@@ -79,7 +79,7 @@
    "outputs": [],
    "source": [
     "# creating the appliance\n",
-    "fridge = household.Appliance(\n",
+    "fridge = household.add_appliance(\n",
     "    name=\"Fridge\",\n",
     "    number=1,\n",
     "    power=200,\n",
@@ -123,30 +123,18 @@
     "# assiging the specific cycles\n",
     "# first cycle: standard cycle\n",
     "fridge.specific_cycle_1(\n",
-    "    p_11=200, # power level for the first operation segment\n",
+    "    p_11=200,  # power level for the first operation segment\n",
     "    t_11=10,  # duration of the first operation segment\n",
-    "    p_12=5,   # power level for the second operation segment\n",
+    "    p_12=5,  # power level for the second operation segment\n",
     "    t_12=20,  # duration of the second operation segment\n",
-    "    r_c1=0    # random variability assigned to the duration of each segment\n",
+    "    r_c1=0,  # random variability assigned to the duration of each segment\n",
     ")\n",
     "\n",
     "# second cycle: intermediate cycle\n",
-    "fridge.specific_cycle_2(\n",
-    "    p_21=200,\n",
-    "    t_21=15,\n",
-    "    p_22=5,\n",
-    "    t_22=15,\n",
-    "    r_c2=0\n",
-    ")\n",
+    "fridge.specific_cycle_2(p_21=200, t_21=15, p_22=5, t_22=15, r_c2=0)\n",
     "\n",
     "# third cycle: intensive cycle\n",
-    "fridge.specific_cycle_3(\n",
-    "    p_31=200,\n",
-    "    t_31=20,\n",
-    "    p_32=5,\n",
-    "    t_32=10,\n",
-    "    r_c3=0\n",
-    ")"
+    "fridge.specific_cycle_3(p_31=200, t_31=20, p_32=5, t_32=10, r_c3=0)"
    ]
   },
   {
@@ -166,7 +154,7 @@
    "source": [
     "# defining cycle behaviour\n",
     "fridge.cycle_behaviour(\n",
-    "     cw11=[0, 299], cw12=[1201, 1440], cw21=[300, 479], cw31=[480, 1200]\n",
+    "    cw11=[0, 299], cw12=[1201, 1440], cw21=[300, 479], cw31=[480, 1200]\n",
     ")"
    ]
   },
@@ -198,7 +186,7 @@
    "source": [
     "# days to build the profiles\n",
     "days = [\n",
-    "    \"2020-12-16\", # a day in the 'Hot' season, assuming a location in the Southern hemisphere\n",
+    "    \"2020-12-16\",  # a day in the 'Hot' season, assuming a location in the Southern hemisphere\n",
     "]\n",
     "\n",
     "profiles = pd.DataFrame(index=range(0, 1440), columns=days)\n",

diff --git a/docs/notebooks/occasional_use.ipynb b/docs/notebooks/occasional_use.ipynb
@@ -62,10 +62,10 @@
     "    number=1,\n",
     "    power=50,\n",
     "    num_windows=1,\n",
-    "    func_time=210, # 3.5 hours\n",
-    "    func_cycle=210,  \n",
+    "    func_time=210,  # 3.5 hours\n",
+    "    func_cycle=210,\n",
     "    occasional_use=0.5,  # 50% chance of occasional use,\n",
-    "    window_1=[480, 750], # start from 8AM\n",
+    "    window_1=[480, 750],  # start from 8AM\n",
     ")"
    ]
   },
@@ -81,10 +81,10 @@
     "    number=1,\n",
     "    power=50,\n",
     "    num_windows=1,\n",
-    "    func_time=210, # 3.5 hours\n",
-    "    func_cycle=210,  \n",
+    "    func_time=210,  # 3.5 hours\n",
+    "    func_cycle=210,\n",
     "    occasional_use=1,  # always present in the mix of appliances,\n",
-    "    window_1=[480, 750], # start from 8AM\n",
+    "    window_1=[480, 750],  # start from 8AM\n",
     ")"
    ]
   },
@@ -117,14 +117,10 @@
     "household_profiles = []\n",
     "school_profiles = []\n",
     "\n",
-    "for day in range(1,number_of_days+1):\n",
-    "    household_profiles.extend(\n",
-    "        household.generate_single_load_profile(prof_i=day)\n",
-    "    )\n",
+    "for day in range(1, number_of_days + 1):\n",
+    "    household_profiles.extend(household.generate_single_load_profile(prof_i=day))\n",
     "\n",
-    "    school_profiles.extend(\n",
-    "        school.generate_single_load_profile(prof_i=day)\n",
-    "    )"
+    "    school_profiles.extend(school.generate_single_load_profile(prof_i=day))"
    ]
   },
   {
@@ -145,20 +141,20 @@
     }
    ],
    "source": [
-    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 4)) \n",
+    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 4))\n",
     "\n",
     "\n",
     "i = 0\n",
     "for name, df in dict(\n",
     "    household_profiles=pd.DataFrame(household_profiles),\n",
-    "    school_profiles=pd.DataFrame(school_profiles)\n",
-    "    ).items():\n",
-    "    df.plot(ax=axes[i],legend=False)\n",
+    "    school_profiles=pd.DataFrame(school_profiles),\n",
+    ").items():\n",
+    "    df.plot(ax=axes[i], legend=False)\n",
     "    axes[i].set_title(name)\n",
-    "    i+=1\n",
+    "    i += 1\n",
     "\n",
     "plt.tight_layout()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {

diff --git a/docs/notebooks/random_seed.ipynb b/docs/notebooks/random_seed.ipynb
@@ -51,10 +51,7 @@
     "from ramp.example.input_file_1 import User_list\n",
     "\n",
     "# Build use case 1 with non-fixed seed\n",
-    "uc_1 = UseCase(\n",
-    "    users=deepcopy(User_list),\n",
-    "    random_seed=None\n",
-    ")\n",
+    "uc_1 = UseCase(users=deepcopy(User_list), random_seed=None)\n",
     "# Initialize and generate load profile\n",
     "uc_1.initialize(peak_enlarge=0.15, num_days=3)\n",
     "uc_1_lp = uc_1.generate_daily_load_profiles()"
@@ -92,19 +89,13 @@
    "cell_type": "code",
    "source": [
     "# Build use case 2 and fixed random seed\n",
-    "uc_2 = UseCase(\n",
-    "    users=deepcopy(User_list),\n",
-    "    random_seed=1\n",
-    ")\n",
+    "uc_2 = UseCase(users=deepcopy(User_list), random_seed=1)\n",
     "# Initialize and generate load profile\n",
     "uc_2.initialize(peak_enlarge=0.15, num_days=3)\n",
     "uc_2_lp = uc_2.generate_daily_load_profiles()\n",
     "\n",
     "# Build use case 3 and same fixed random seed as uc_2\n",
-    "uc_3 = UseCase(\n",
-    "    users=deepcopy(User_list),\n",
-    "    random_seed=1\n",
-    ")\n",
+    "uc_3 = UseCase(users=deepcopy(User_list), random_seed=1)\n",
     "\n",
     "# Initialize and generate load profile\n",
     "uc_3.initialize(peak_enlarge=0.15, num_days=3)\n",
@@ -147,12 +138,17 @@
     "import matplotlib.pyplot as plt\n",
     "import pandas as pd\n",
     "\n",
-    "lp_df = pd.DataFrame({'UseCase_1: non_fixed_seed': uc_1_lp,\n",
-    "                      'UseCase_2: fixed_seed': uc_2_lp,\n",
-    "                      'UseCase_3: fixed_seed': uc_3_lp,\n",
-    "                      'diff UseCase_2 - UseCase_1': uc_2_lp - uc_1_lp,  # difference between uc_1 and uc_2 is not zero\n",
-    "                      'diff UseCase_2 - UseCase_3': uc_2_lp - uc_3_lp  # difference between uc_2 and uc_3 is zero\n",
-    "                      })\n",
+    "lp_df = pd.DataFrame(\n",
+    "    {\n",
+    "        \"UseCase_1: non_fixed_seed\": uc_1_lp,\n",
+    "        \"UseCase_2: fixed_seed\": uc_2_lp,\n",
+    "        \"UseCase_3: fixed_seed\": uc_3_lp,\n",
+    "        \"diff UseCase_2 - UseCase_1\": uc_2_lp\n",
+    "        - uc_1_lp,  # difference between uc_1 and uc_2 is not zero\n",
+    "        \"diff UseCase_2 - UseCase_3\": uc_2_lp\n",
+    "        - uc_3_lp,  # difference between uc_2 and uc_3 is zero\n",
+    "    }\n",
+    ")\n",
     "lp_df.plot()\n",
     "plt.show()"
    ],

diff --git a/docs/notebooks/simple_bulb.ipynb b/docs/notebooks/simple_bulb.ipynb
@@ -69,16 +69,16 @@
    "source": [
     "# add_appliance is meth\n",
     "indoor_bulb = household.add_appliance(\n",
-    "    name=\"Indoor Light Bulb\", # the name of the appliance\n",
-    "    number=6, # how many of this appliance each user has in this user category\n",
-    "    power=7, # the power (in Watt) of each single appliance. RAMP does not deal with units of measures, you should check the consistency of the unit of measures throughout your model\n",
-    "    num_windows=2, # how many usage time windows throughout the day?\n",
-    "    func_time=120, # the total usage time of appliances\n",
-    "    func_cycle=10, # the minimum usage time after a switch on event\n",
+    "    name=\"Indoor Light Bulb\",  # the name of the appliance\n",
+    "    number=6,  # how many of this appliance each user has in this user category\n",
+    "    power=7,  # the power (in Watt) of each single appliance. RAMP does not deal with units of measures, you should check the consistency of the unit of measures throughout your model\n",
+    "    num_windows=2,  # how many usage time windows throughout the day?\n",
+    "    func_time=120,  # the total usage time of appliances\n",
+    "    func_cycle=10,  # the minimum usage time after a switch on event\n",
     "    window_1=[0, 30],  # from 24 to 00:30\n",
     "    window_2=[1170, 1440],  # from 19:30 to 24:00\n",
-    "    random_var_w=0.35, # Variability of the windows in percentage\n",
-    "    time_fraction_random_variability=0.2, # randomizes the total time the appliance is on (between 0 and 1)\n",
+    "    random_var_w=0.35,  # Variability of the windows in percentage\n",
+    "    time_fraction_random_variability=0.2,  # randomizes the total time the appliance is on (between 0 and 1)\n",
     ")"
    ]
   },
@@ -186,10 +186,12 @@
    ],
    "source": [
     "use_case = UseCase(\n",
-    "    users=[household], # A list of all the user categories to be included in the simulation. In this case, we only have household user category\n",
-    "    date_start=\"2020-01-01\", # starting date of the simulation\n",
-    "    date_end=\"2020-12-31\", # end date of the simulation\n",
-    "    )\n"
+    "    users=[\n",
+    "        household\n",
+    "    ],  # A list of all the user categories to be included in the simulation. In this case, we only have household user category\n",
+    "    date_start=\"2020-01-01\",  # starting date of the simulation\n",
+    "    date_end=\"2020-12-31\",  # end date of the simulation\n",
+    ")"
    ]
   },
   {
@@ -283,9 +285,7 @@
     }
    ],
    "source": [
-    "single_profile = household.generate_single_load_profile(\n",
-    "    day_type=0\n",
-    "    )"
+    "single_profile = household.generate_single_load_profile(day_type=0)"
    ]
   },
   {
@@ -337,9 +337,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "aggregated_profile = household.generate_aggregated_load_profile(\n",
-    "    day_type=0\n",
-    ")"
+    "aggregated_profile = household.generate_aggregated_load_profile(day_type=0)"
    ]
   },
   {