# %%
from collections import defaultdict

import altair as alt
import matplotlib.pyplot as plt
import pandas as pd
from cmap import Colormap
import polars as pl

#%%

df = pl.read_csv("example_data_bk.csv")
df.columns

df_crop = df[:, :3]
df_crop

df_crop.write_csv("example_data.csv")
#%%

# %%
kwargs = {"comment": "#", "header": [0, 1], "index_col": 0}
df = pd.read_csv("fit_result_batch.csv", **kwargs)
# %%
df
# %%


df_wt = df["SecB WT apo"].reset_index()
df_dimer = df["SecB his dimer apo"].reset_index()

AA_categories = {
    "pos": ["R", "H", "K"],
    "neg": ["D", "E"],
    "aromatic": ["F", "W", "Y"],
    "polar": ["S", "T", "N", "Q"],
    "nonpolar": ["A", "V", "I", "L", "M"],
    "other": ["G", "C", "P"],
}
cat_list = list(AA_categories)
AA_lut = {aa: category for category in AA_categories for aa in AA_categories[category]}
AA_lut
aa_cat_numbers = [cat_list.index(AA_lut[aa]) for aa in df_wt["sequence"]]
df_wt["aa_cat"] = aa_cat_numbers

# %%
cmap = Colormap("colorbrewer:Accent_6")
sol = defaultdict(list)
colors = cmap(df_wt["aa_cat"])

nums = range(6)
colors = cmap(nums)

for n, c in zip(nums, colors):
    print(n, c)
# %%
len(cmap.color_stops)

colors = cmap.to_altair(N=cmap.num_colors)
domain = range(6)
altair_scale = alt.Scale(domain=domain, range=colors, clamp=True)
# %%
alt.Chart(df_wt).mark_point().encode(
    x="r_number",
    y="aa_cat",
    color=alt.Color("aa_cat:N", scale=altair_scale),
)

# %%
import pandas as pd

df = pd.DataFrame({"a": [1, 2, 3, 4], "b": [7, 6, 5, 4], "c": ["a", "b", "b", "c"]})

chart = alt.Chart(df).mark_point().encode(alt.X("a"), alt.Y("b"), alt.Color("c:N"))
chart
# %%


# %%


ddG = df_wt["deltaG"] - df_dimer["deltaG"]
ddG


# %%

fig, ax = plt.subplots()
ax.scatter(df_wt["r_number"], df_wt["deltaG"])

# %%
fig, ax = plt.subplots()
ax.scatter(df_wt["r_number"], ddG)

# %%
df_wt.columns
# %%

output = pd.DataFrame(
    {
        "r_number": df_wt["r_number"],
        "SecB tetramer ΔG": df_wt["deltaG"],
        "dimer ΔΔG": ddG,
        "aa_category": df_wt["aa_cat"],
    }
)
output = output.set_index("r_number")

output
# %%
import numpy as np

N = 150
fuzzy_sin = 0.5 * (1 + np.sin(np.arange(N) / 10.0)) + np.random.normal(
    loc=0, scale=0.1, size=N
)
df = pd.DataFrame({"fuzzy_sin": fuzzy_sin})
df



# add series to output dataframe a a column

output["fuzzy_sin"] = series
output
# %%

output.to_csv("SecB_data.csv")
# %%

dir(cmap)
cmap.category

# %%

tol_cmap = Colormap("tol:rainbow_discrete_7")
tol_cmap.category
tol_cmap.num_colors
tol_cmap.interpolation

# %%

tol_cmap = Colormap("vispy:hsl")
tol_cmap.category
tol_cmap.num_colors
tol_cmap.interpolation


# %%
tol_cmap = Colormap("yorick:stern")
tol_cmap.category
tol_cmap.num_colors
tol_cmap.interpolation
# %%
tol_cmap = Colormap("tol:rainbow_whbr")
tol_cmap.category
tol_cmap.num_colors
tol_cmap.interpolation
# %%
tol_cmap = Colormap("glasbey:glasbey")
tol_cmap.category
tol_cmap.num_colors
# tol_cmap.interpolation


# %%