add jupyter to toml files (#283)

* feat: Add dptbplas example
>
> This commit introduces a new example demonstrating the integration of DeePTB with TBPLaS for calculating the AC conductivity of crystalline silicon using two different methods:
>
> 1.  **Lindhard function method:** An accurate but computationally expensive method performed in k-space.
> 2.  **TBPM method:** A linear-scaling method performed in real-space on a supercell, suitable for large and complex systems.
>
> The example includes a Jupyter notebook (deeptb_tbplas.ipynb) that walks through the calculation process for both methods, along with the necessary input files (conv2.vasp and mix.latest.pth).
>
> This example serves as a tutorial for users to understand the workflow of using DeePTB models with TBPLaS and to explore the effects of different computational parameters on the results.

* docs: add TBPlaS tutorial and update index

* docs: 添加 GEMINI 文档，概述项目结构和使用说明

* feat: Introduce `uv` for dependency management and centralize installation logic into `install.sh` while updating Dockerfiles and documentation.

* feat: explicitly specify the Python executable for `uv sync` and print it during installation.

* feat: add validation for install script variant parameter

* feat: Add several new Python package dependencies and update the lock file.

Author: QG-phy

dptb/__main__.py

@@ -7,26 +7,31 @@
 log = logging.getLogger(__name__)
 
 def print_logo():
-    f = pyfiglet.Figlet(font='dos_rebel')  # 您可以选择您喜欢的字体
-    logo = f.renderText("DeePTB")
-    log.info(" ")
-    log.info(" ")
-    log.info("#"*81)
-    log.info("#" + " "*79 + "#")
-    log.info("#" + " "*79 + "#")
-    for line in logo.split('\n'):
-        if line.strip():  # 避免记录空行
-            log.info('#     '+line+ '     #')
-    log.info("#" + " "*79 + "#")
-    version_info = f"Version: {__version__}"
-    padding = (79 - len(version_info)) // 2
-    nspace = 79-padding
-    format_str = "#" + "{}"+"{:<"+f"{nspace}" + "}"+ "#"         
-    log.info(format_str.format(" "*padding, version_info))
-    log.info("#" + " "*79 + "#")
-    log.info("#"*81)
-    log.info(" ")
-    log.info(" ")
+    try:
+        from rich.console import Console, Group
+        from rich.panel import Panel
+        from rich.align import Align
+        from rich.text import Text
+        # Create Figlet with classic dos_rebel font
+        f = pyfiglet.Figlet(font='dos_rebel')
+        logo_text = f.renderText("DeePTB")
+        # Build Rich components with centered version
+        logo = Align.center(Text(logo_text, style="bold cyan"))
+        version = Align.center(Text(f"Version: {__version__}", style="bold white"))
+        panel = Panel(
+            Group(logo, version),
+            border_style="blue",
+            padding=(1, 2),
+            title="Deep Learning Tight-Binding",
+            subtitle="https://github.com/deepmodeling/DeePTB"
+        )
+        console = Console()
+        console.print(panel)
+    except ImportError:
+        # Fallback if rich is not installed (though it should be)
+        f = pyfiglet.Figlet(font='dos_rebel')
+        print(f.renderText("DeePTB"))
+        print(f"Version: {__version__}")
 def main() -> None:
     """
     The main entry point for the dptb package.

pyproject.toml

@@ -44,6 +44,7 @@ dependencies = [
     "pyfiglet==1.0.2",
     "tensorboard",
     "seekpath",
+    "rich>=13.0.0",
 ]
 
 [project.optional-dependencies]
@@ -75,3 +76,10 @@ fallback_version = "0.0.0-dev"  # Fallback for non-git installs (e.g. zip downlo
 #   uv sync --find-links https://data.pyg.org/whl/torch-2.5.0+cu121.html
 #   OR use the helper script: ./install.sh cu121
 find-links = ["https://data.pyg.org/whl/torch-2.5.0+cpu.html"]
+
+[dependency-groups]
+dev = [
+    "ipykernel",
+    "jupyter",
+    "notebook",
+]