{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "!rm .fleche -rf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Started with Fleche"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook demonstrates the main features of the `fleche` library, a caching library for Python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Long-running calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No config file found. Using default memory cache.\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "from fleche import fleche, cache, tags, project\n",
    "from fleche.digest import Digest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "@fleche\n",
    "def long_running_calculation(x):\n",
    "    print(f'Running calculation for {x}...')\n",
    "    time.sleep(2)\n",
    "    return x * x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running calculation for 2...\n",
      "First call took 2.00 seconds.\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "long_running_calculation(2)\n",
    "end = time.time()\n",
    "print(f'First call took {end - start:.2f} seconds.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Second call took 0.00 seconds.\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "long_running_calculation(2)\n",
    "end = time.time()\n",
    "print(f'Second call took {end - start:.2f} seconds.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running calculation for 100...\n",
      "Second call took 2.00 seconds.\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "long_running_calculation(100)\n",
    "end = time.time()\n",
    "print(f'Second call took {end - start:.2f} seconds.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the second call returns almost instantly, because the result was cached.\n",
    "As soon as the argument changes, fleche runs the original fucntion again."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recursive function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "@fleche\n",
    "def fib(n):\n",
    "    if n < 2:\n",
    "        return n\n",
    "    return fib(n-1) + fib(n-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fib(20) took 0.0067 seconds with caching.\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "fib(20)\n",
    "end = time.time()\n",
    "print(f'fib(20) took {end - start:.4f} seconds with caching.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Without caching, this would be much slower as each call to `fib` would be recomputed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Passing Digests as Arguments\n",
    "\n",
    "`fleche` supports passing `Digest` objects directly to cached functions. When a function receives a `Digest`, `fleche` automatically expands it to its actual value from the cache before executing the function. You can use the convenience wrapper `D` to mark a string as a digest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running calculation for 10...\n",
      "Value Digest: 60079f7901a9295349d1796c037afc132e81286f785ddeeb763104ef02363102\n",
      "Short digest: 60079f79\n",
      "Doubling 100...\n",
      "Result: 200\n"
     ]
    }
   ],
   "source": [
    "from fleche import D\n",
    "from fleche.digest import digest as value_digest\n",
    "\n",
    "@fleche\n",
    "def double(x):\n",
    "    print(f\"Doubling {x}...\")\n",
    "    return x * 2\n",
    "\n",
    "# 1. Run the calculation to ensure it is cached\n",
    "long_running_calculation(10)\n",
    "\n",
    "# 2. Compute the value digest for 100 (the cached result)\n",
    "v = long_running_calculation(10)\n",
    "val_dig = value_digest(v)\n",
    "print(f\"Value Digest: {val_dig}\")\n",
    "\n",
    "# 3. Pass a short digest prefix of the value to double(); it will expand to 100.\n",
    "short = str(val_dig)[:8]\n",
    "print(f\"Short digest: {short}\")\n",
    "print(f\"Result: {double(D(short))}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Querying Cached Calls "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### via Function Wrapper\n",
    "\n",
    "You can retrieve previously cached calls that match some of your function's arguments and metadata using the function wrapper's `query` method. Any field left as `None` is treated as a wildcard. Arguments and result are compared by digest internally, but the wrapper decodes them back to Python objects when returning matches.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Query returns an iterator object, that also defines additional utilities, e.g. to create a table of queried calls, do"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "                                                                        name  \\\n",
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       "\n",
       "                                                      module     timestart  \\\n",
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       "\n",
       "                                                        timestop  walltime  \n",
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     "execution_count": 10,
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    }
   ],
   "source": [
    "long_running_calculation.query().table()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The table includes the call digest as an index and the metadata associated with the call.\n",
    "\n",
    "Arguments can be selectively included in the table via the `arguments`.  You only pay the loading cost for the specified arguments, not for arguments that are not included in the table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "                                                                        name  \\\n",
       "4435e2927c194eff1bb90270ab3f353f0db1a8b50ec0493...  long_running_calculation   \n",
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       "\n",
       "                                                      module    x  \\\n",
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       "\n",
       "                                                       timestart  \\\n",
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       "                                                        timestop  walltime  \n",
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     "execution_count": 11,
     "metadata": {},
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   ],
   "source": [
    "long_running_calculation.query().table(arguments=['x'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "                                                                        name  \\\n",
       "4435e2927c194eff1bb90270ab3f353f0db1a8b50ec0493...  long_running_calculation   \n",
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       "\n",
       "                                                      module  result    x  \\\n",
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       "\n",
       "                                                       timestart  \\\n",
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       "\n",
       "                                                        timestop  walltime  \n",
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       "a7ce6824785adc406ca3561dcf98b3c64ddf1539d2467e1...  1.774227e+09  2.000230  "
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     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "long_running_calculation.query().table(arguments=['x'], results=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### via Cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fleche.call import QueryCall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "    <tr>\n",
       "      <th>698e29f05ba00ee23503848cd166215f62cd976d54431594036979d8d56f254f</th>\n",
       "      <td>fib</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405dfbadf453a9d5bbe3482fbb05251a6fc18b90045f8b4edeafb1c6f236fc80</th>\n",
       "      <td>fib</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>0.000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24231d9bc47f7abc0ea485d178fc8457dce8082790f8c948b936ebe906352225</th>\n",
       "      <td>fib</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>0.000838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
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       "                                                                        name  \\\n",
       "4435e2927c194eff1bb90270ab3f353f0db1a8b50ec0493...  long_running_calculation   \n",
       "cb9db168b7a8eb99bda637a54241ad5d4e2726b5cd67ad4...  long_running_calculation   \n",
       "698e29f05ba00ee23503848cd166215f62cd976d5443159...                       fib   \n",
       "405dfbadf453a9d5bbe3482fbb05251a6fc18b90045f8b4...                       fib   \n",
       "24231d9bc47f7abc0ea485d178fc8457dce8082790f8c94...                       fib   \n",
       "\n",
       "                                                      module     timestart  \\\n",
       "4435e2927c194eff1bb90270ab3f353f0db1a8b50ec0493...  __main__  1.774227e+09   \n",
       "cb9db168b7a8eb99bda637a54241ad5d4e2726b5cd67ad4...  __main__  1.774227e+09   \n",
       "698e29f05ba00ee23503848cd166215f62cd976d5443159...  __main__  1.774227e+09   \n",
       "405dfbadf453a9d5bbe3482fbb05251a6fc18b90045f8b4...  __main__  1.774227e+09   \n",
       "24231d9bc47f7abc0ea485d178fc8457dce8082790f8c94...  __main__  1.774227e+09   \n",
       "\n",
       "                                                        timestop  walltime  \n",
       "4435e2927c194eff1bb90270ab3f353f0db1a8b50ec0493...  1.774227e+09  2.000136  \n",
       "cb9db168b7a8eb99bda637a54241ad5d4e2726b5cd67ad4...  1.774227e+09  2.000227  \n",
       "698e29f05ba00ee23503848cd166215f62cd976d5443159...  1.774227e+09  0.000004  \n",
       "405dfbadf453a9d5bbe3482fbb05251a6fc18b90045f8b4...  1.774227e+09  0.000002  \n",
       "24231d9bc47f7abc0ea485d178fc8457dce8082790f8c94...  1.774227e+09  0.000838  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cache().query(QueryCall(module=\"__main__\")).table().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>405dfbadf453a9d5bbe3482fbb05251a6fc18b90045f8b4edeafb1c6f236fc80</th>\n",
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       "      <td>__main__</td>\n",
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       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
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       "      <td>0.000838</td>\n",
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       "    <tr>\n",
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       "      <td>fib</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>0.001454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e2b3a4eaf9036f83560677b9bad64cbb8263cf3496ba6faeefc15b4231178912</th>\n",
       "      <td>fib</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>0.001772</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   name    module  \\\n",
       "698e29f05ba00ee23503848cd166215f62cd976d5443159...  fib  __main__   \n",
       "405dfbadf453a9d5bbe3482fbb05251a6fc18b90045f8b4...  fib  __main__   \n",
       "24231d9bc47f7abc0ea485d178fc8457dce8082790f8c94...  fib  __main__   \n",
       "dabebddba19859bdb1a075e422b1842dde433c7d6ca51c2...  fib  __main__   \n",
       "e2b3a4eaf9036f83560677b9bad64cbb8263cf3496ba6fa...  fib  __main__   \n",
       "\n",
       "                                                       timestart  \\\n",
       "698e29f05ba00ee23503848cd166215f62cd976d5443159...  1.774227e+09   \n",
       "405dfbadf453a9d5bbe3482fbb05251a6fc18b90045f8b4...  1.774227e+09   \n",
       "24231d9bc47f7abc0ea485d178fc8457dce8082790f8c94...  1.774227e+09   \n",
       "dabebddba19859bdb1a075e422b1842dde433c7d6ca51c2...  1.774227e+09   \n",
       "e2b3a4eaf9036f83560677b9bad64cbb8263cf3496ba6fa...  1.774227e+09   \n",
       "\n",
       "                                                        timestop  walltime  \n",
       "698e29f05ba00ee23503848cd166215f62cd976d5443159...  1.774227e+09  0.000004  \n",
       "405dfbadf453a9d5bbe3482fbb05251a6fc18b90045f8b4...  1.774227e+09  0.000002  \n",
       "24231d9bc47f7abc0ea485d178fc8457dce8082790f8c94...  1.774227e+09  0.000838  \n",
       "dabebddba19859bdb1a075e422b1842dde433c7d6ca51c2...  1.774227e+09  0.001454  \n",
       "e2b3a4eaf9036f83560677b9bad64cbb8263cf3496ba6fa...  1.774227e+09  0.001772  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cache().query(QueryCall(name=\"fib\")).table().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>cb9db168b7a8eb99bda637a54241ad5d4e2726b5cd67ad4cfb21baa5874c2b9e</th>\n",
       "      <td>long_running_calculation</td>\n",
       "      <td>__main__</td>\n",
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       "    <tr>\n",
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       "      <td>0.000019</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "                                                                        name  \\\n",
       "cb9db168b7a8eb99bda637a54241ad5d4e2726b5cd67ad4...  long_running_calculation   \n",
       "3fb9bd0ba9c388530e4a0f5fbb789aaf7b5c598c1fae748...                    double   \n",
       "\n",
       "                                                      module     timestart  \\\n",
       "cb9db168b7a8eb99bda637a54241ad5d4e2726b5cd67ad4...  __main__  1.774227e+09   \n",
       "3fb9bd0ba9c388530e4a0f5fbb789aaf7b5c598c1fae748...  __main__  1.774227e+09   \n",
       "\n",
       "                                                        timestop  walltime  \n",
       "cb9db168b7a8eb99bda637a54241ad5d4e2726b5cd67ad4...  1.774227e+09  2.000227  \n",
       "3fb9bd0ba9c388530e4a0f5fbb789aaf7b5c598c1fae748...  1.774227e+09  0.000019  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cache().query(QueryCall(arguments={\"x\": 100})).table()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`fleche` allows you to add metadata to your cached functions using the `tags` context manager. This can be useful for organizing and querying your results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "@fleche\n",
    "def another_calculation(a, b):\n",
    "    return a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "with tags(project='my_project', category='testing'):\n",
    "    another_calculation(1, 2)\n",
    "    another_calculation(3, 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This metadata is stored alongside the cached result. This metadata can be used to query the cache as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "another_calculation LazyArguments({'a': 'da217d50752f3371d9f8b62a3e72409592bd34b74e14fdac43e2b137bd59f21f', 'b': '92a9b214d814a4a7b5f9ba52e2248c6d16ec0196d8cd7798b345471118bf0c67'}) {'project': 'my_project', 'category': 'testing'}\n",
      "LazyArguments({'a': '65d52a82c5a72f12ca0499522dc9274a0e6822e1038630ba68f94400b3e4c98f', 'b': '83ada2198553b88cb3d0882f7fca8c4e9531049b978df3e9e3b5d6301c6c0bfa'}) 7\n"
     ]
    }
   ],
   "source": [
    "# Query by metadata presence (tags) and a specific key-value filter\n",
    "for call in another_calculation.query(1, 2, metadata={\"tags\": {}}):\n",
    "    # presence-only: any call with 'tags'\n",
    "    print(call.name, call.arguments, call.metadata.get(\"tags\"))\n",
    "\n",
    "for call in another_calculation.query(3, 4, metadata={\"tags\": {\"project\": \"my_project\"}}):\n",
    "    # equality filter on metadata\n",
    "    assert call.metadata[\"tags\"][\"project\"] == \"my_project\"\n",
    "    # arguments and result are decoded if they were stored as digests\n",
    "    print(call.arguments, call.result)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Caching Methods of User-defined Types\n",
    "\n",
    "`fleche` can also cache methods of classes. For this to work, the class must be \"digest-compatible\". You can make a class digest-compatible by implementing a `__digest__` method or by using a `dataclass`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyClass:\n",
    "    def __init__(self, val):\n",
    "        self.val = val\n",
    "    \n",
    "    def __digest__(self):\n",
    "        # The digest defines how the instance is identified in the cache\n",
    "        return Digest(str(self.val))\n",
    "\n",
    "    @fleche\n",
    "    def compute(self, x):\n",
    "        print(f\"Computing {self.val} + {x}...\")\n",
    "        time.sleep(1)\n",
    "        return self.val + x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing 10 + 5...\n",
      "Result: 15\n",
      "First call took 1.00 seconds.\n",
      "Result: 15\n",
      "Second call (same instance) took 0.00 seconds.\n"
     ]
    }
   ],
   "source": [
    "obj = MyClass(10)\n",
    "\n",
    "start = time.time()\n",
    "print(f\"Result: {obj.compute(5)}\")\n",
    "print(f\"First call took {time.time() - start:.2f} seconds.\")\n",
    "\n",
    "start = time.time()\n",
    "print(f\"Result: {obj.compute(5)}\")\n",
    "print(f\"Second call (same instance) took {time.time() - start:.2f} seconds.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you mutate the instance such that its digest changes, the cache will be missed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing 20 + 5...\n",
      "Result: 25\n",
      "Call after mutation took 1.00 seconds.\n"
     ]
    }
   ],
   "source": [
    "obj.val = 20\n",
    "start = time.time()\n",
    "print(f\"Result: {obj.compute(5)}\")\n",
    "print(f\"Call after mutation took {time.time() - start:.2f} seconds.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Passing Digests as Arguments\n",
    "\n",
    "`fleche` supports passing `Digest` objects directly to cached functions. When a function receives a `Digest`, `fleche` automatically expands it to its actual value from the cache before executing the function. You can use the convenience wrapper `D` to mark a string as a digest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value Digest: 60079f7901a9295349d1796c037afc132e81286f785ddeeb763104ef02363102\n",
      "Short digest: 60079f79\n",
      "Result: 200\n"
     ]
    }
   ],
   "source": [
    "from fleche import D\n",
    "from fleche.digest import digest as value_digest\n",
    "\n",
    "@fleche\n",
    "def double(x):\n",
    "    print(f\"Doubling {x}...\")\n",
    "    return x * 2\n",
    "\n",
    "# 1. Run the calculation to ensure it is cached\n",
    "long_running_calculation(10)\n",
    "\n",
    "# 2. Compute the value digest for 100 (the cached result)\n",
    "v = long_running_calculation(10)\n",
    "val_dig = value_digest(v)\n",
    "print(f\"Value Digest: {val_dig}\")\n",
    "\n",
    "# 3. Pass a short digest prefix of the value to double(); it will expand to 100.\n",
    "short = str(val_dig)[:8]\n",
    "print(f\"Short digest: {short}\")\n",
    "print(f\"Result: {double(D(short))}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`fleche` allows you to add metadata to your cached functions using the `tags` context manager. This can be useful for organizing and querying your results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "@fleche\n",
    "def another_calculation(a, b):\n",
    "    return a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "with tags(project='my_project', category='testing'):\n",
    "    another_calculation(1, 2)\n",
    "    another_calculation(3, 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This metadata is stored alongside the cached result. You can then use the `metadata.table` method to view the metadata for all cached results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>698e29f05ba00ee23503848cd166215f62cd976d54431594036979d8d56f254f</th>\n",
       "      <td>fib</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405dfbadf453a9d5bbe3482fbb05251a6fc18b90045f8b4edeafb1c6f236fc80</th>\n",
       "      <td>fib</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24231d9bc47f7abc0ea485d178fc8457dce8082790f8c948b936ebe906352225</th>\n",
       "      <td>fib</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>0.000838</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dabebddba19859bdb1a075e422b1842dde433c7d6ca51c2b9a284dc1bb743d79</th>\n",
       "      <td>fib</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>0.001454</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e2b3a4eaf9036f83560677b9bad64cbb8263cf3496ba6faeefc15b4231178912</th>\n",
       "      <td>fib</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>0.001772</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981a54293b3027931a47d21c275955d6dab2fe6d8fa4c2f4fcd3190b6187b0ac</th>\n",
       "      <td>fib</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>0.002040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fdb0a9b8b1df91293924ad8fc03bcf041f3e50f924e9d199b8606eed60579d91</th>\n",
       "      <td>fib</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>0.002305</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>db6b5632c77a01a692e13c486c54536a0c46907972d93900a60a1eab59110b93</th>\n",
       "      <td>fib</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>0.002581</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cd94bda61e484ca82c470b6ab50c0f1b98055e6a905d82f18b57abc5e07b6457</th>\n",
       "      <td>fib</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>0.002940</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f4efb0e68400d195d8d57051088964d98b9ae4f7dbb78dad4feb055b56fd47de</th>\n",
       "      <td>fib</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>0.003206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bc7f8b67d293d9fbdec343f49c44d46e655516537cc492ba3a990edb7f86f176</th>\n",
       "      <td>fib</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>0.003480</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6fbe26ceafe80276c7714b2aae14c25bd38e24683d6624f01322946f5e53e7cf</th>\n",
       "      <td>fib</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>0.003752</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f3cefd42fd07119792c38084ecbb33f1955ccd83d8021e1bd078db3377cca4c3</th>\n",
       "      <td>fib</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>0.004020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2b7c9e8c5e0a73afddb28a49d47415462b3c84adc05c35567b81514b90c053f2</th>\n",
       "      <td>fib</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>0.004293</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60bf26e7cdec62d1a8f9ebfbebb336850cf588d547badc7a18a0001fd0b65cf9</th>\n",
       "      <td>fib</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>0.004573</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9b23a905e639149d35169118ffefadec03a181cbd290006620e671655b1499c3</th>\n",
       "      <td>fib</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>0.004852</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18e77dab7a4a0385a66512547336e7a02ee62803634fd801e6dd49c6e5444e2f</th>\n",
       "      <td>fib</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>0.005127</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e1a78503325bcbb94116e03febed590b14bdd7f955c4f15b8649e613fbd6bf5c</th>\n",
       "      <td>fib</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>0.005413</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34814b3262b7920c7cef43eaf8a1e86e849611ed0d0317c4342b29231a8213ea</th>\n",
       "      <td>fib</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>0.005702</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9214caae9e9819230882f7e9f0f1970fd5f2fc354c3404d1fd7ef30aa2c75873</th>\n",
       "      <td>fib</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>0.005992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c5a5bb6cf5b69c7eb5e244ed540af44391dff12a77fcecf19505cc660b50e6d9</th>\n",
       "      <td>fib</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>0.006301</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a7ce6824785adc406ca3561dcf98b3c64ddf1539d2467e1b9e6318e4a97368e8</th>\n",
       "      <td>long_running_calculation</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>2.000230</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3fb9bd0ba9c388530e4a0f5fbb789aaf7b5c598c1fae748de84aad8f244a2d8f</th>\n",
       "      <td>double</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a8a3061653183cff08e5c414f9a4087550ab5e33cc186ae7a2e91aa1fbac8c80</th>\n",
       "      <td>another_calculation</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>my_project</td>\n",
       "      <td>testing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1e352b538b9219d4e5fcaf429882a4b3587f3bc6358ef0feca453b301b40156a</th>\n",
       "      <td>another_calculation</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>my_project</td>\n",
       "      <td>testing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81d4df70836ded7d8a0dd70348976c431210ff824d870acb2932b984e7936e14</th>\n",
       "      <td>compute</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.000169</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e9f9ca077d0566f335e54df480d202815d7b5b5ac5e7ccf7ba47c5bf2fa219ae</th>\n",
       "      <td>compute</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.000242</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                        name  \\\n",
       "4435e2927c194eff1bb90270ab3f353f0db1a8b50ec0493...  long_running_calculation   \n",
       "cb9db168b7a8eb99bda637a54241ad5d4e2726b5cd67ad4...  long_running_calculation   \n",
       "698e29f05ba00ee23503848cd166215f62cd976d5443159...                       fib   \n",
       "405dfbadf453a9d5bbe3482fbb05251a6fc18b90045f8b4...                       fib   \n",
       "24231d9bc47f7abc0ea485d178fc8457dce8082790f8c94...                       fib   \n",
       "dabebddba19859bdb1a075e422b1842dde433c7d6ca51c2...                       fib   \n",
       "e2b3a4eaf9036f83560677b9bad64cbb8263cf3496ba6fa...                       fib   \n",
       "981a54293b3027931a47d21c275955d6dab2fe6d8fa4c2f...                       fib   \n",
       "fdb0a9b8b1df91293924ad8fc03bcf041f3e50f924e9d19...                       fib   \n",
       "db6b5632c77a01a692e13c486c54536a0c46907972d9390...                       fib   \n",
       "cd94bda61e484ca82c470b6ab50c0f1b98055e6a905d82f...                       fib   \n",
       "f4efb0e68400d195d8d57051088964d98b9ae4f7dbb78da...                       fib   \n",
       "bc7f8b67d293d9fbdec343f49c44d46e655516537cc492b...                       fib   \n",
       "6fbe26ceafe80276c7714b2aae14c25bd38e24683d6624f...                       fib   \n",
       "f3cefd42fd07119792c38084ecbb33f1955ccd83d8021e1...                       fib   \n",
       "2b7c9e8c5e0a73afddb28a49d47415462b3c84adc05c355...                       fib   \n",
       "60bf26e7cdec62d1a8f9ebfbebb336850cf588d547badc7...                       fib   \n",
       "9b23a905e639149d35169118ffefadec03a181cbd290006...                       fib   \n",
       "18e77dab7a4a0385a66512547336e7a02ee62803634fd80...                       fib   \n",
       "e1a78503325bcbb94116e03febed590b14bdd7f955c4f15...                       fib   \n",
       "34814b3262b7920c7cef43eaf8a1e86e849611ed0d0317c...                       fib   \n",
       "9214caae9e9819230882f7e9f0f1970fd5f2fc354c3404d...                       fib   \n",
       "c5a5bb6cf5b69c7eb5e244ed540af44391dff12a77fcecf...                       fib   \n",
       "a7ce6824785adc406ca3561dcf98b3c64ddf1539d2467e1...  long_running_calculation   \n",
       "3fb9bd0ba9c388530e4a0f5fbb789aaf7b5c598c1fae748...                    double   \n",
       "a8a3061653183cff08e5c414f9a4087550ab5e33cc186ae...       another_calculation   \n",
       "1e352b538b9219d4e5fcaf429882a4b3587f3bc6358ef0f...       another_calculation   \n",
       "81d4df70836ded7d8a0dd70348976c431210ff824d870ac...                   compute   \n",
       "e9f9ca077d0566f335e54df480d202815d7b5b5ac5e7ccf...                   compute   \n",
       "\n",
       "                                                      module     timestart  \\\n",
       "4435e2927c194eff1bb90270ab3f353f0db1a8b50ec0493...  __main__  1.774227e+09   \n",
       "cb9db168b7a8eb99bda637a54241ad5d4e2726b5cd67ad4...  __main__  1.774227e+09   \n",
       "698e29f05ba00ee23503848cd166215f62cd976d5443159...  __main__  1.774227e+09   \n",
       "405dfbadf453a9d5bbe3482fbb05251a6fc18b90045f8b4...  __main__  1.774227e+09   \n",
       "24231d9bc47f7abc0ea485d178fc8457dce8082790f8c94...  __main__  1.774227e+09   \n",
       "dabebddba19859bdb1a075e422b1842dde433c7d6ca51c2...  __main__  1.774227e+09   \n",
       "e2b3a4eaf9036f83560677b9bad64cbb8263cf3496ba6fa...  __main__  1.774227e+09   \n",
       "981a54293b3027931a47d21c275955d6dab2fe6d8fa4c2f...  __main__  1.774227e+09   \n",
       "fdb0a9b8b1df91293924ad8fc03bcf041f3e50f924e9d19...  __main__  1.774227e+09   \n",
       "db6b5632c77a01a692e13c486c54536a0c46907972d9390...  __main__  1.774227e+09   \n",
       "cd94bda61e484ca82c470b6ab50c0f1b98055e6a905d82f...  __main__  1.774227e+09   \n",
       "f4efb0e68400d195d8d57051088964d98b9ae4f7dbb78da...  __main__  1.774227e+09   \n",
       "bc7f8b67d293d9fbdec343f49c44d46e655516537cc492b...  __main__  1.774227e+09   \n",
       "6fbe26ceafe80276c7714b2aae14c25bd38e24683d6624f...  __main__  1.774227e+09   \n",
       "f3cefd42fd07119792c38084ecbb33f1955ccd83d8021e1...  __main__  1.774227e+09   \n",
       "2b7c9e8c5e0a73afddb28a49d47415462b3c84adc05c355...  __main__  1.774227e+09   \n",
       "60bf26e7cdec62d1a8f9ebfbebb336850cf588d547badc7...  __main__  1.774227e+09   \n",
       "9b23a905e639149d35169118ffefadec03a181cbd290006...  __main__  1.774227e+09   \n",
       "18e77dab7a4a0385a66512547336e7a02ee62803634fd80...  __main__  1.774227e+09   \n",
       "e1a78503325bcbb94116e03febed590b14bdd7f955c4f15...  __main__  1.774227e+09   \n",
       "34814b3262b7920c7cef43eaf8a1e86e849611ed0d0317c...  __main__  1.774227e+09   \n",
       "9214caae9e9819230882f7e9f0f1970fd5f2fc354c3404d...  __main__  1.774227e+09   \n",
       "c5a5bb6cf5b69c7eb5e244ed540af44391dff12a77fcecf...  __main__  1.774227e+09   \n",
       "a7ce6824785adc406ca3561dcf98b3c64ddf1539d2467e1...  __main__  1.774227e+09   \n",
       "3fb9bd0ba9c388530e4a0f5fbb789aaf7b5c598c1fae748...  __main__  1.774227e+09   \n",
       "a8a3061653183cff08e5c414f9a4087550ab5e33cc186ae...  __main__  1.774227e+09   \n",
       "1e352b538b9219d4e5fcaf429882a4b3587f3bc6358ef0f...  __main__  1.774227e+09   \n",
       "81d4df70836ded7d8a0dd70348976c431210ff824d870ac...  __main__  1.774227e+09   \n",
       "e9f9ca077d0566f335e54df480d202815d7b5b5ac5e7ccf...  __main__  1.774227e+09   \n",
       "\n",
       "                                                        timestop  walltime  \\\n",
       "4435e2927c194eff1bb90270ab3f353f0db1a8b50ec0493...  1.774227e+09  2.000136   \n",
       "cb9db168b7a8eb99bda637a54241ad5d4e2726b5cd67ad4...  1.774227e+09  2.000227   \n",
       "698e29f05ba00ee23503848cd166215f62cd976d5443159...  1.774227e+09  0.000004   \n",
       "405dfbadf453a9d5bbe3482fbb05251a6fc18b90045f8b4...  1.774227e+09  0.000002   \n",
       "24231d9bc47f7abc0ea485d178fc8457dce8082790f8c94...  1.774227e+09  0.000838   \n",
       "dabebddba19859bdb1a075e422b1842dde433c7d6ca51c2...  1.774227e+09  0.001454   \n",
       "e2b3a4eaf9036f83560677b9bad64cbb8263cf3496ba6fa...  1.774227e+09  0.001772   \n",
       "981a54293b3027931a47d21c275955d6dab2fe6d8fa4c2f...  1.774227e+09  0.002040   \n",
       "fdb0a9b8b1df91293924ad8fc03bcf041f3e50f924e9d19...  1.774227e+09  0.002305   \n",
       "db6b5632c77a01a692e13c486c54536a0c46907972d9390...  1.774227e+09  0.002581   \n",
       "cd94bda61e484ca82c470b6ab50c0f1b98055e6a905d82f...  1.774227e+09  0.002940   \n",
       "f4efb0e68400d195d8d57051088964d98b9ae4f7dbb78da...  1.774227e+09  0.003206   \n",
       "bc7f8b67d293d9fbdec343f49c44d46e655516537cc492b...  1.774227e+09  0.003480   \n",
       "6fbe26ceafe80276c7714b2aae14c25bd38e24683d6624f...  1.774227e+09  0.003752   \n",
       "f3cefd42fd07119792c38084ecbb33f1955ccd83d8021e1...  1.774227e+09  0.004020   \n",
       "2b7c9e8c5e0a73afddb28a49d47415462b3c84adc05c355...  1.774227e+09  0.004293   \n",
       "60bf26e7cdec62d1a8f9ebfbebb336850cf588d547badc7...  1.774227e+09  0.004573   \n",
       "9b23a905e639149d35169118ffefadec03a181cbd290006...  1.774227e+09  0.004852   \n",
       "18e77dab7a4a0385a66512547336e7a02ee62803634fd80...  1.774227e+09  0.005127   \n",
       "e1a78503325bcbb94116e03febed590b14bdd7f955c4f15...  1.774227e+09  0.005413   \n",
       "34814b3262b7920c7cef43eaf8a1e86e849611ed0d0317c...  1.774227e+09  0.005702   \n",
       "9214caae9e9819230882f7e9f0f1970fd5f2fc354c3404d...  1.774227e+09  0.005992   \n",
       "c5a5bb6cf5b69c7eb5e244ed540af44391dff12a77fcecf...  1.774227e+09  0.006301   \n",
       "a7ce6824785adc406ca3561dcf98b3c64ddf1539d2467e1...  1.774227e+09  2.000230   \n",
       "3fb9bd0ba9c388530e4a0f5fbb789aaf7b5c598c1fae748...  1.774227e+09  0.000019   \n",
       "a8a3061653183cff08e5c414f9a4087550ab5e33cc186ae...  1.774227e+09  0.000011   \n",
       "1e352b538b9219d4e5fcaf429882a4b3587f3bc6358ef0f...  1.774227e+09  0.000025   \n",
       "81d4df70836ded7d8a0dd70348976c431210ff824d870ac...  1.774227e+09  1.000169   \n",
       "e9f9ca077d0566f335e54df480d202815d7b5b5ac5e7ccf...  1.774227e+09  1.000242   \n",
       "\n",
       "                                                       project category  \n",
       "4435e2927c194eff1bb90270ab3f353f0db1a8b50ec0493...         NaN      NaN  \n",
       "cb9db168b7a8eb99bda637a54241ad5d4e2726b5cd67ad4...         NaN      NaN  \n",
       "698e29f05ba00ee23503848cd166215f62cd976d5443159...         NaN      NaN  \n",
       "405dfbadf453a9d5bbe3482fbb05251a6fc18b90045f8b4...         NaN      NaN  \n",
       "24231d9bc47f7abc0ea485d178fc8457dce8082790f8c94...         NaN      NaN  \n",
       "dabebddba19859bdb1a075e422b1842dde433c7d6ca51c2...         NaN      NaN  \n",
       "e2b3a4eaf9036f83560677b9bad64cbb8263cf3496ba6fa...         NaN      NaN  \n",
       "981a54293b3027931a47d21c275955d6dab2fe6d8fa4c2f...         NaN      NaN  \n",
       "fdb0a9b8b1df91293924ad8fc03bcf041f3e50f924e9d19...         NaN      NaN  \n",
       "db6b5632c77a01a692e13c486c54536a0c46907972d9390...         NaN      NaN  \n",
       "cd94bda61e484ca82c470b6ab50c0f1b98055e6a905d82f...         NaN      NaN  \n",
       "f4efb0e68400d195d8d57051088964d98b9ae4f7dbb78da...         NaN      NaN  \n",
       "bc7f8b67d293d9fbdec343f49c44d46e655516537cc492b...         NaN      NaN  \n",
       "6fbe26ceafe80276c7714b2aae14c25bd38e24683d6624f...         NaN      NaN  \n",
       "f3cefd42fd07119792c38084ecbb33f1955ccd83d8021e1...         NaN      NaN  \n",
       "2b7c9e8c5e0a73afddb28a49d47415462b3c84adc05c355...         NaN      NaN  \n",
       "60bf26e7cdec62d1a8f9ebfbebb336850cf588d547badc7...         NaN      NaN  \n",
       "9b23a905e639149d35169118ffefadec03a181cbd290006...         NaN      NaN  \n",
       "18e77dab7a4a0385a66512547336e7a02ee62803634fd80...         NaN      NaN  \n",
       "e1a78503325bcbb94116e03febed590b14bdd7f955c4f15...         NaN      NaN  \n",
       "34814b3262b7920c7cef43eaf8a1e86e849611ed0d0317c...         NaN      NaN  \n",
       "9214caae9e9819230882f7e9f0f1970fd5f2fc354c3404d...         NaN      NaN  \n",
       "c5a5bb6cf5b69c7eb5e244ed540af44391dff12a77fcecf...         NaN      NaN  \n",
       "a7ce6824785adc406ca3561dcf98b3c64ddf1539d2467e1...         NaN      NaN  \n",
       "3fb9bd0ba9c388530e4a0f5fbb789aaf7b5c598c1fae748...         NaN      NaN  \n",
       "a8a3061653183cff08e5c414f9a4087550ab5e33cc186ae...  my_project  testing  \n",
       "1e352b538b9219d4e5fcaf429882a4b3587f3bc6358ef0f...  my_project  testing  \n",
       "81d4df70836ded7d8a0dd70348976c431210ff824d870ac...         NaN      NaN  \n",
       "e9f9ca077d0566f335e54df480d202815d7b5b5ac5e7ccf...         NaN      NaN  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cache().table()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtering\n",
    "\n",
    "The metadata table is just pandas so you can query and filter as you like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>module</th>\n",
       "      <th>timestart</th>\n",
       "      <th>timestop</th>\n",
       "      <th>walltime</th>\n",
       "      <th>project</th>\n",
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       "      <th>4435e2927c194eff1bb90270ab3f353f0db1a8b50ec04932de88f7a4e5745953</th>\n",
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       "      <td>long_running_calculation</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>2.000230</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>double</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a8a3061653183cff08e5c414f9a4087550ab5e33cc186ae7a2e91aa1fbac8c80</th>\n",
       "      <td>another_calculation</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>my_project</td>\n",
       "      <td>testing</td>\n",
       "    </tr>\n",
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       "      <th>1e352b538b9219d4e5fcaf429882a4b3587f3bc6358ef0feca453b301b40156a</th>\n",
       "      <td>another_calculation</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>my_project</td>\n",
       "      <td>testing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81d4df70836ded7d8a0dd70348976c431210ff824d870acb2932b984e7936e14</th>\n",
       "      <td>compute</td>\n",
       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.000169</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e9f9ca077d0566f335e54df480d202815d7b5b5ac5e7ccf7ba47c5bf2fa219ae</th>\n",
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       "      <td>__main__</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.774227e+09</td>\n",
       "      <td>1.000242</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                        name  \\\n",
       "4435e2927c194eff1bb90270ab3f353f0db1a8b50ec0493...  long_running_calculation   \n",
       "cb9db168b7a8eb99bda637a54241ad5d4e2726b5cd67ad4...  long_running_calculation   \n",
       "a7ce6824785adc406ca3561dcf98b3c64ddf1539d2467e1...  long_running_calculation   \n",
       "3fb9bd0ba9c388530e4a0f5fbb789aaf7b5c598c1fae748...                    double   \n",
       "a8a3061653183cff08e5c414f9a4087550ab5e33cc186ae...       another_calculation   \n",
       "1e352b538b9219d4e5fcaf429882a4b3587f3bc6358ef0f...       another_calculation   \n",
       "81d4df70836ded7d8a0dd70348976c431210ff824d870ac...                   compute   \n",
       "e9f9ca077d0566f335e54df480d202815d7b5b5ac5e7ccf...                   compute   \n",
       "\n",
       "                                                      module     timestart  \\\n",
       "4435e2927c194eff1bb90270ab3f353f0db1a8b50ec0493...  __main__  1.774227e+09   \n",
       "cb9db168b7a8eb99bda637a54241ad5d4e2726b5cd67ad4...  __main__  1.774227e+09   \n",
       "a7ce6824785adc406ca3561dcf98b3c64ddf1539d2467e1...  __main__  1.774227e+09   \n",
       "3fb9bd0ba9c388530e4a0f5fbb789aaf7b5c598c1fae748...  __main__  1.774227e+09   \n",
       "a8a3061653183cff08e5c414f9a4087550ab5e33cc186ae...  __main__  1.774227e+09   \n",
       "1e352b538b9219d4e5fcaf429882a4b3587f3bc6358ef0f...  __main__  1.774227e+09   \n",
       "81d4df70836ded7d8a0dd70348976c431210ff824d870ac...  __main__  1.774227e+09   \n",
       "e9f9ca077d0566f335e54df480d202815d7b5b5ac5e7ccf...  __main__  1.774227e+09   \n",
       "\n",
       "                                                        timestop  walltime  \\\n",
       "4435e2927c194eff1bb90270ab3f353f0db1a8b50ec0493...  1.774227e+09  2.000136   \n",
       "cb9db168b7a8eb99bda637a54241ad5d4e2726b5cd67ad4...  1.774227e+09  2.000227   \n",
       "a7ce6824785adc406ca3561dcf98b3c64ddf1539d2467e1...  1.774227e+09  2.000230   \n",
       "3fb9bd0ba9c388530e4a0f5fbb789aaf7b5c598c1fae748...  1.774227e+09  0.000019   \n",
       "a8a3061653183cff08e5c414f9a4087550ab5e33cc186ae...  1.774227e+09  0.000011   \n",
       "1e352b538b9219d4e5fcaf429882a4b3587f3bc6358ef0f...  1.774227e+09  0.000025   \n",
       "81d4df70836ded7d8a0dd70348976c431210ff824d870ac...  1.774227e+09  1.000169   \n",
       "e9f9ca077d0566f335e54df480d202815d7b5b5ac5e7ccf...  1.774227e+09  1.000242   \n",
       "\n",
       "                                                       project category  \n",
       "4435e2927c194eff1bb90270ab3f353f0db1a8b50ec0493...         NaN      NaN  \n",
       "cb9db168b7a8eb99bda637a54241ad5d4e2726b5cd67ad4...         NaN      NaN  \n",
       "a7ce6824785adc406ca3561dcf98b3c64ddf1539d2467e1...         NaN      NaN  \n",
       "3fb9bd0ba9c388530e4a0f5fbb789aaf7b5c598c1fae748...         NaN      NaN  \n",
       "a8a3061653183cff08e5c414f9a4087550ab5e33cc186ae...  my_project  testing  \n",
       "1e352b538b9219d4e5fcaf429882a4b3587f3bc6358ef0f...  my_project  testing  \n",
       "81d4df70836ded7d8a0dd70348976c431210ff824d870ac...         NaN      NaN  \n",
       "e9f9ca077d0566f335e54df480d202815d7b5b5ac5e7ccf...         NaN      NaN  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cache().table().query('name!=\"fib\"')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Querying Cached Calls via Function Wrapper\n",
    "\n",
    "You can retrieve previously cached calls that match some of your function's arguments and metadata using the function wrapper's `query` method. Any field left as `None` is treated as a wildcard. Arguments and result are compared by digest internally, but the wrapper decodes them back to Python objects when returning matches.\n",
    "\n",
    "Example using the `another_calculation` wrapper we created above:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "another_calculation LazyArguments({'a': 'da217d50752f3371d9f8b62a3e72409592bd34b74e14fdac43e2b137bd59f21f', 'b': '92a9b214d814a4a7b5f9ba52e2248c6d16ec0196d8cd7798b345471118bf0c67'}) {'project': 'my_project', 'category': 'testing'}\n",
      "LazyArguments({'a': '65d52a82c5a72f12ca0499522dc9274a0e6822e1038630ba68f94400b3e4c98f', 'b': '83ada2198553b88cb3d0882f7fca8c4e9531049b978df3e9e3b5d6301c6c0bfa'}) 7\n"
     ]
    }
   ],
   "source": [
    "# Query by metadata presence (tags) and a specific key-value filter\n",
    "for call in another_calculation.query(1, 2, metadata={\"tags\": {}}):\n",
    "    # presence-only: any call with 'tags'\n",
    "    print(call.name, call.arguments, call.metadata.get(\"tags\"))\n",
    "\n",
    "for call in another_calculation.query(3, 4, metadata={\"tags\": {\"project\": \"my_project\"}}):\n",
    "    # equality filter on metadata\n",
    "    assert call.metadata[\"tags\"][\"project\"] == \"my_project\"\n",
    "    # arguments and result are decoded if they were stored as digests\n",
    "    print(call.arguments, call.result)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lazy Loading\n",
    "\n",
    "When you load a call from the cache, fleche returns it as a `LazyCall` by default. Arguments and results are only fetched from storage when you actually access them \u2014 so iterating over a large cache or inspecting metadata stays fast even when individual results are huge.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obtained LazyCall for fib(20)\n",
      "Accessing result now (triggers load)...\n",
      "Result: 6765\n"
     ]
    }
   ],
   "source": [
    "# Default load: returns a LazyCall \u2014 no deserialization yet\n",
    "key = fib.digest(20)\n",
    "lazy_call = cache().load(key)\n",
    "print(f\"Got a {type(lazy_call).__name__} for {lazy_call.name}(20)\")\n",
    "\n",
    "# Accessing .result triggers the actual load from storage\n",
    "print(f\"Result: {lazy_call.result}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To load everything upfront instead, pass `lazy=False` or call `.fetch()` on a lazy call you already have.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Both of these produce a fully-loaded Call:\n",
    "cache().load(key, lazy=False) == lazy_call.fetch()\n"
   ]
  }
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