Update src/app.py

src/app.py

@@ -307,7 +307,7 @@ with tab1:
             index=0,
             key="enc_scheme",
             help=(
-                "**6-bit LNS** – Custom 56-character table (A-Z, 0-9, punctuation). 6 bits/char.\n\n"
+                "**6-bit LNS** – Custom 57-character table (A-Z, 0-9, punctuation). 6 bits/char.\n\n"
                 "**Base64 (6-bit)** – Standard Base64 encoding of UTF-8 bytes. 6 bits/symbol.\n\n"
                 "**ASCII (7-bit)** – Standard 7-bit ASCII. 7 bits/char.\n\n"
                 "**UTF-8 (8-bit)** – Full UTF-8 byte encoding. 8 bits/byte. Supports all Unicode."
@@ -1063,13 +1063,13 @@ with tab3:
 
             transform_option = st.selectbox(
                 "Value transformation:",
-                ["Raw (linear)", "log1p", "log1p → log1p"],
+                ["Raw (linear)", "ln(1+x)", "ln(ln(1+x))"],
                 index=0,
                 key="transform_select",
                 help=(
                     "**Raw** - No transformation.\n\n"
-                    "**log1p** - `log(1 + x)`. Compresses high values, spreads low range.\n\n"
-                    "**log1p → log1p** - Double log1p. Even stronger compression.\n\n"
+                    "**ln(1+x)** - `ln(1 + x)`. Compresses high values, spreads low range.\n\n"
+                    "**ln(ln(1+x))** - Double ln. Even stronger compression.\n\n"
                 )
             )
 
@@ -1086,14 +1086,14 @@ with tab3:
             #             result[col] = result[col] - med
             #     return result
 
-            if transform_option == "log1p":
+            if transform_option == "ln(1+x)":
                 transformed = np.log1p(pos_data)
-                value_label = "Editing Value (log1p)"
-                transform_tag = "log1p"
-            elif transform_option == "log1p → log1p":
+                value_label = "Editing Value (ln(1+x))"
+                transform_tag = "ln(1+x)"
+            elif transform_option == "ln(ln(1+x))":
                 transformed = np.log1p(np.log1p(pos_data))
-                value_label = "Editing Value (log1p → log1p)"
-                transform_tag = "log1p_log1p"
+                value_label = "Editing Value (ln(ln(1+x)))"
+                transform_tag = "ln(ln(1+x))"
             # elif transform_option == "log1p → pos. norm.":
             #     transformed = robust_pos_normalize_log1p(pos_data)
             #     value_label = "Editing Value (log1p → pos. norm.)"
@@ -1108,33 +1108,71 @@ with tab3:
                 lambda x: int(re.search(r"(\d+)", str(x)).group(1)) if re.search(r"(\d+)", str(x)) else 0
             )
 
+
             st.markdown("#### 📊 Histogram - All Values")
 
             n_bins = st.number_input("Number of bins:", min_value=10, max_value=300, value=80, step=10, key="hist_bins")
 
-            fig2, ax2 = plt.subplots(figsize=(10, 4))
-            ax2.hist(melted["Value"].values, bins=n_bins, color="#4F46E5", edgecolor="white", linewidth=0.3)
-            ax2.set_xlabel(value_label)
-            ax2.set_ylabel("Count")
+            matplotlib.rcParams["font.family"] = "Arial"
+            matplotlib.rcParams["font.sans-serif"] = ["Arial", "DejaVu Sans", "Liberation Sans", "sans-serif"]
+
+            fig2, ax2 = plt.subplots(figsize=(7, 5))
+            all_vals = melted["Value"].values
+            ax2.hist(all_vals, bins=n_bins, color="#808080", alpha=0.8, label="All")
+            ax2.set_xlim(left=0.0)
+            ax2.xaxis.set_major_locator(MultipleLocator(0.5))
+            ax2.xaxis.set_minor_locator(MultipleLocator(0.1))
+            ax2.tick_params(axis="x", which="major", length=6)
+            ax2.tick_params(axis="x", which="minor", length=3)
+            ax2.legend(fontsize=8)
             ax2.set_title(f"Raw Values Distribution ({transform_tag})")
-            val_min = melted["Value"].min()
-            val_max = melted["Value"].max()
-            val_range = val_max - val_min
-            if val_range <= 2:
-                tick_step = 0.1
-            elif val_range <= 6:
-                tick_step = 0.2
-            elif val_range <= 20:
-                tick_step = 1
-            else:
-                tick_step = 5
-            ax2.set_xticks(np.arange(np.floor(val_min / tick_step) * tick_step,
-                                     val_max + tick_step, tick_step))
-            ax2.tick_params(axis='x', labelsize=8, rotation=45)
-            ax2.grid(axis='y', alpha=0.3)
+            ax2.set_xlabel(value_label)
+            ax2.set_ylabel("Counts")
+
+            # Apply axis styling
+            for attr in [ax2.xaxis.label, ax2.yaxis.label, ax2.title]:
+                attr.set_fontsize(12)
+                attr.set_fontweight("bold")
+                attr.set_fontfamily("Arial")
+            for spine in ax2.spines.values():
+                spine.set_linewidth(1.5)
+            ax2.tick_params(axis="both", which="both", width=1.2, labelsize=10)
+            ax2.spines['top'].set_visible(False)
+            ax2.spines['right'].set_visible(False)
+            for ticklab in ax2.get_xticklabels() + ax2.get_yticklabels():
+                ticklab.set_fontfamily("Arial")
+                ticklab.set_fontweight("normal")
+
             fig2.tight_layout()
             st.pyplot(fig2)
 
+            # st.markdown("#### 📊 Histogram - All Values")
+
+            # n_bins = st.number_input("Number of bins:", min_value=10, max_value=300, value=80, step=10, key="hist_bins")
+
+            # fig2, ax2 = plt.subplots(figsize=(10, 4))
+            # ax2.hist(melted["Value"].values, bins=n_bins, color="#4F46E5", edgecolor="white", linewidth=0.3)
+            # ax2.set_xlabel(value_label)
+            # ax2.set_ylabel("Count")
+            # ax2.set_title(f"Raw Values Distribution ({transform_tag})")
+            # val_min = melted["Value"].min()
+            # val_max = melted["Value"].max()
+            # val_range = val_max - val_min
+            # if val_range <= 2:
+            #     tick_step = 0.1
+            # elif val_range <= 6:
+            #     tick_step = 0.2
+            # elif val_range <= 20:
+            #     tick_step = 1
+            # else:
+            #     tick_step = 5
+            # ax2.set_xticks(np.arange(np.floor(val_min / tick_step) * tick_step,
+            #                          val_max + tick_step, tick_step))
+            # ax2.tick_params(axis='x', labelsize=8, rotation=45)
+            # ax2.grid(axis='y', alpha=0.3)
+            # fig2.tight_layout()
+            st.pyplot(fig2)
+
             st.markdown("#### 2️⃣ Density Scatter Plot")
             st.caption("Each dot = one measurement (sample × position). Color = local point density.")