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Remove usage of deprecated User.Appliance class #142

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merged 8 commits into from
May 14, 2024
Merged

Remove usage of deprecated User.Appliance class #142

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0a29465
Remove deprecated code usage
Bachibouzouk May 6, 2024
458487c
Copy the appliance parameter docstrings to add_appliance docstring
Bachibouzouk May 6, 2024
1686ef3
Correct argument name typo
Bachibouzouk May 13, 2024
f4cd862
Update the documentation
Bachibouzouk May 13, 2024
505e4aa
Explicit the arguments in methods the example files
Bachibouzouk May 13, 2024
681388e
Freeze black version for CI and add jupyter linting
Bachibouzouk May 14, 2024
43883ce
Lint with black
Bachibouzouk May 14, 2024
7ad9f39
Update documentation notebooks
Bachibouzouk May 14, 2024
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2 changes: 1 addition & 1 deletion .github/workflows/pytest.yml
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Expand Up @@ -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
Expand Down
35 changes: 19 additions & 16 deletions docs/notebooks/cooking_app.ipynb
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Expand Up @@ -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",
")"
]
},
Expand Down Expand Up @@ -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())"
]
},
{
Expand All @@ -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()"
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21 changes: 9 additions & 12 deletions docs/notebooks/fixed_flat_app.ipynb
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Expand Up @@ -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",
")"
]
},
{
Expand Down Expand Up @@ -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)"
]
},
{
Expand Down Expand Up @@ -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",
Expand Down
28 changes: 8 additions & 20 deletions docs/notebooks/multi_cycle.ipynb
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Expand Up @@ -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",
Expand Down Expand Up @@ -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)"
]
},
{
Expand All @@ -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",
")"
]
},
Expand Down Expand Up @@ -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",
Expand Down
34 changes: 15 additions & 19 deletions docs/notebooks/occasional_use.ipynb
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Expand Up @@ -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",
")"
]
},
Expand All @@ -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",
")"
]
},
Expand Down Expand Up @@ -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))"
]
},
{
Expand All @@ -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()"
]
},
{
Expand Down
32 changes: 14 additions & 18 deletions docs/notebooks/random_seed.ipynb
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Expand Up @@ -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()"
Expand Down Expand Up @@ -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",
Expand Down Expand Up @@ -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()"
],
Expand Down
34 changes: 16 additions & 18 deletions docs/notebooks/simple_bulb.ipynb
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Expand Up @@ -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",
")"
]
},
Expand Down Expand Up @@ -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",
")"
]
},
{
Expand Down Expand Up @@ -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)"
]
},
{
Expand Down Expand Up @@ -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)"
]
},
{
Expand Down
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