ci: add pep8-naming check to flake8 and ignore a bunch of warnings

README.md

@@ -418,51 +418,51 @@ where `scenario` is a `Scenario` instance.
 #### I. Accessing and Saving Relevant Supply Information
 Analyzing generator supply and cost curves requires the proper generator cost and plant information to be accessed from a Grid object. This data can be accessed using the following:
 ```python
-supply_df = powersimdata.design.generation.cost_curves.get_supply_data(grid, num_segments)
-```
-where `grid` is a `Grid` object and `num_segments` is the number of linearized cost curve segments into which the provided quadratic cost curve should be split.
+from powersimdata.design.generation.cost_curves import get_supply_data
 
-The above returns a data frame that contains information about each generator's fuel type, quadratic cost curve, and linearized cost curve, as well as the interconnect and load zone to which the generator belongs. The above function can store the data frame as a CSV file if `save` is passed a valid file path and file name string in `get_supply_data`; by default, `save=None`. The `get_supply_data` function is used within many of the following supply and cost curve visualization and analysis functions.
+supply_df = get_supply_data(grid, num_segments, save)
+```
+where `grid` is a `Grid` object, `num_segments` is the number of linearized cost curve segments into which the provided quadratic cost curve should be split, and `save` is a string representing the desired file path and file name to which the resulting data will be saved. `save` defaults to `None`. `get_supply_data` returns a DataFrame that contains information about each generator's fuel type, quadratic cost curve, and linearized cost curve, as well as the interconnect and load zone to which the generator belongs. `get_supply_data` is used within many of the following supply and cost curve visualization and analysis functions.
 
 
 #### II. Visualizing Generator Supply Curves
 To obtain the supply curve for a particular fuel type and area, the following is used:
 ```python
-P, F = powersimdata.design.generation.cost_curves.build_supply_curve(grid, num_segments, area, type)
-```
-where `grid` is a `Grid` object; `num_segments` is the number of linearized cost curve segments to create; `area` is a string describing an appropriate load zone, interconnect, or state; and `type` is a string describing an appropriate fuel type.
+from powersimdata.design.generation.cost_curves import build_supply_curve
 
-By default, the above function plots the created supply curve (plotting can be suppressed by including `plot=False` in `build_supply_curve`). `P` and `F`, the supply curve capacity and bid quantities, respectively, are also returned. This function also allows for the area type (e.g., load zone, state, and interconnect are different area types) to be specified. By default, the area type is inferred, though there are instances where specifying the area type can be useful (e.g., Texas can refer to both a state and an interconnect, though they are not the same thing). To specify the area type, `area_type` must be passed a valid area type string in `build_supply_curve`.
+P, F = build_supply_curve(grid, num_segments, area, gen_type, area_type, plot)
+```
+where `grid` is a `Grid` object; `num_segments` is the number of linearized cost curve segments to create; `area` is a string describing an appropriate load zone, interconnect, or state; `gen_type` is a string describing an appropriate fuel type; `area_type` is a string describing the type of region that is being considered; and `plot` is a boolean that indicates whether or not the plot is shown. `area_type` defaults to `None`, which allows the area type to be inferred; there are instances where specifying the area type can be useful (e.g., Texas can refer to both a state and an interconnect, though they are not the same thing). `plot` defaults to `True`. `build_supply_curve` returns `P` and `F`, the supply curve capacity and price quantities, respectively.
 
 
 #### III. Comparing Supply Curves
 When updating generator cost curve information, it can be useful to see the corresponding effect on the supply curve for a particular area and fuel type pair. Instead of only performing a visual inspection between the original and new supply curves, the maximum price difference between the two supply curves can be calculated. This metric, which is similar to the Kolmogorov-Smirnov test, serves as a goodness-of-fit test between the two supply curves, where a lower score is desired. This metric can be calculated as follows:
 ```python
-max_diff = powersimdata.design.generation.cost_curves.ks_test(P1, F1, P2, F2)
-```
-where `P1` and `P2` are lists containing supply curve capacity data and `F1` and `F2` are lists containing corresponding supply curve price data. These lists can be created using `build_supply_curve` or can be created manually.
+from powersimdata.design.generation.cost_curves import ks_test
 
-It should be noted that the two supply curves must offer the same amount of capacity (i.e., `max(P1) = max(P2)`). By default, the above function plots the two supply curves overlaid on a single plot (plotting can be suppressed by including `plot=False` in `ks_test()`).
+max_diff = ks_test(P1, F1, P2, F2, area, gen_type, plot)
+```
+where `P1` and `P2` are lists containing supply curve capacity data; `F1` and `F2` are lists containing corresponding supply curve price data; `area` is a string describing an appropriate load zone, interconnect, or state; `gen_type` is a string describing an appropriate fuel type; and `plot` is a boolean that indicates whether or not the plot is shown. The pairs of supply curve data, (`P1`, `F1`) and (`P2`, `F2`), can be created using `build_supply_curve` or can be created manually.  It should be noted that the two supply curves must offer the same amount of capacity (i.e., `max(P1) = max(P2)`). `area` and `gen_type` both default to `None`. `plot` defaults to `True`. `ks_test` returns `max_diff`, which is the maximum price difference between the two supply curves.
 
 
 #### IV. Comparing Cost Curve Parameters
-When designing generator cost curves, it can be instructive to visually compare the quadratic cost curve parameters for generators in a particular area and fuel type pair. The `c1` and `c2` parameters for a given area and fuel type can be compared in a plot using the following:
+When designing generator cost curves, it can be instructive to visually compare the quadratic cost curve parameters for generators in a particular area and fuel type pair. The linear terms (`c1`) and quadratic terms (`c2`) for a given area and fuel type can be compared in a plot using the following:
 ```python
-powersimdata.design.generation.cost_curves.plot_c1_vs_c2(grid, area, type)
-```
-where `grid` is a `Grid` object; `area` is a string describing an appropriate load zone, interconnect, or state; and `type` is a string describing an appropriate fuel type.
+from powersimdata.design.generation.cost_curves import plot_linear_vs_quadratic_terms
 
-This function features a zoom capability (enabled by including `zoom=True` in `plot_c1_vs_c2`) that filters out `c2` outliers to enable better visualization. `c2` outliers outside of a specified number of standard deviations (the default is `num_sd=3`) are filtered out. The desired number of standard deviations can be changed by defining `num_sd` in `plot_c1_vs_c2`. Similar to `build_supply_curve`, this function also provides users with the ability to specify a particular area type.
+plot_linear_vs_quadratic_terms(grid, area, gen_type, area_type, plot, zoom, num_sd, alpha)
+```
+where `grid` is a `Grid` object; `area` is a string describing an appropriate load zone, interconnect, or state; `gen_type` is a string describing an appropriate fuel type; `area_type` is a string describing the type of region that is being considered; `plot` is a boolean that indicates whether or not the plot is shown; `zoom` is a boolean that indicates whether or not the zoom capability that filters out quadratic term outliers for better visualization is enabled; `num_sd` is the number of standard deviations outside of which quadratic terms are filtered; and `alpha` is the alpha blending parameter for the scatter plot. `area_type` defaults to `None`, which allows the area type to be inferred. `plot` defaults to `True`. `zoom` defaults to `False`. `num_sd` defaults to `3`. `alpha`, which can take values between `0` and `1`, defaults to `0.1`. 
 
 
 #### V. Comparing Generators by Capacity and Price
 When designing generator cost curves, it can be useful to visually compare the capacity and price parameters for each generator in a specified area and fuel type pair. The generator capacity and price parameters for a given area and fuel type can be compared in a plot using the following:
 ```python
-powersimdata.design.generation.cost_curves.plot_capacity_vs_price(grid, num_segments, area, type)
-```
-where `grid` is a `Grid` object; `num_segments` is the number of linearized cost curve segments to create; `area` is a string describing an appropriate load zone, interconnect, or state; and `type` is a string describing an appropriate fuel type.
+from powersimdata.design.generation.cost_curves import plot_capacity_vs_price
 
-Similar to `build_supply_curve` and `plot_c1_vs_c2`, this function also provides users with the ability to specify a particular area type.
+plot_capacity_vs_price(grid, num_segments, area, gen_type, area_type, plot)
+```
+where `grid` is a `Grid` object; `num_segments` is the number of linearized cost curve segments to create; `area` is a string describing an appropriate load zone, interconnect, or state; `gen_type` is a string describing an appropriate fuel type; `area_type` is a string describing the type of region that is being considered; and `plot` is a boolean that indicates whether or not the plot is shown. `area_type` defaults to `None`, which allows the area type to be inferred. `plot` defaults to `True`.
 
 
 [PreREISE]: https://github.com/Breakthrough-Energy/PreREISE