Delete interactive.py

interactive.py

@@ -1,166 +0,0 @@
-# %%
-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
-
-
-# %%