print("starting folder: {}".format(loc))

csv_files = []

if isinstance(loc, str):

loc = [loc]

for loc_i in loc:

if os.path.isdir(loc_i):

new_files = glob.glob(loc_i + "/**/*.csv", recursive=True)

csv_files.extend(new_files)

elif os.path.isfile(loc_i):

csv_files.append(loc_i)

df = pd.concat(map(pd.read_csv, csv_files), ignore_index=True, sort=True)

df.drop_duplicates(inplace=True)

df = df[["Processor", "GPU", "Num Rollouts", "Method", "Mean Optimization Time (ms)", " Std. Dev. Time (ms)"]]

# torchrl_data = df.loc[df["Method"] == "torchrl"]

df = df.sort_values(by=["Num Rollouts"])

methods = df.Method.unique()

cpu_names = df.Processor.unique()

gpu_names = df.GPU.unique()

gpu_names = [gpu for gpu in gpu_names if not pd.isna(gpu)]

cpu_names.sort()

cpu_names = np.flip(cpu_names)

gpu_names.sort()

methods.sort()

colors = ["orange", "red", "green", "blue", "cyan", "xkcd:pink", "xkcd:brown", "xkcd:sky blue", "xkcd:magenta"]

# Create combined items and add both GPU and CPUs to legend

legends = []

i = 0

for cpu in cpu_names:

for gpu in gpu_names:

plt.errorbar("Num Rollouts", "Mean Optimization Time (ms)", yerr=" Std. Dev. Time (ms)",

color=colors[i], capsize=2,

data=df.loc[(df["Method"] == graph_type) & (df["Processor"] == cpu) & (df["GPU"] == gpu)])

legend_name = cpu + "," + gpu

legend_name = legend_name.replace("NVIDIA", "")

legend_name = legend_name.replace(" 6-Core Processor", "")

legend_name = legend_name.replace("5 ", "")

legend_name = legend_name.replace("(R) Core(TM)", "")

legend_name = legend_name.replace("13th Gen ", "")

legends.append(legend_name)

i += 1

plt.legend(labels=legends,fontsize=12)

plt.xscale("log")

# plt.yscale("log")

plt.xlabel("Number of Samples")

plt.ylabel("Optimization Times [ms]")

plt.title("{} across Hardware".format(graph_type))

plt.tight_layout()

file_name_type = graph_type

file_name_type = file_name_type.replace(" ", "_").lower()

file_name_type = file_name_type + "_hw"

print(file_name_type)