Merge branch 'dev' into kzscisoft/filter-object

Author: kzscisoft

.pre-commit-config.yaml

@@ -23,7 +23,7 @@ repos:
         args: [--branch, main, --branch, dev]
       - id: check-added-large-files
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.5.5
+    rev: v0.6.3
     hooks:
       - id: ruff
         args: [ --fix, --exit-non-zero-on-fix, "--ignore=C901" ]

CHANGELOG.md

@@ -1,5 +1,10 @@
 # Change log
 
+## [v1.0.2](https://github.com/simvue-io/client/releases/tag/v1.0.2) - 2024-08-21
+
+* Fix incorrect HTTP status code in `Client` when checking if object exists.
+* Fix issue with `running=False` when launching a `Run` caused by incorrect system metadata being sent to the server.
+
 ## [v1.0.1](https://github.com/simvue-io/client/releases/tag/v1.0.1) - 2024-07-16
 
 * Fix to `add_process` with list of strings as arguments, the executable no longer returns the string `"None"`.
@@ -60,6 +65,7 @@
 * Added logging messages for debugging when debug level set to `debug`.
 
 ## [v0.11.2](https://github.com/simvue-io/client/releases/tag/v0.11.2) - 2023-03-06
+
 * Raise exceptions in `Client` class methods if run does not exist or artifact does not exist.
 * (Bug fix) `list_artifacts` optional category restriction now works.
 

CITATION.cff

@@ -42,6 +42,6 @@ keywords:
   - alerting
   - simulation
 license: Apache-2.0
-commit: 74054e4c586f5b55ff62b95d05ad5fe67d8807f7
-version: 1.0.1
-date-released: '2024-07-16'
+commit: dd47b0c04e5c634a5bb39b216c71a38987de4360
+version: 1.0.2
+date-released: '2024-08-21'

README.md

@@ -15,6 +15,7 @@ Collect metadata, metrics and artifacts from simulations, processing and AI/ML t
 <img src="https://img.shields.io/badge/python-3.9%20%7C%203.10%20%7C%203.11%20%7C%203.12-blue">
 <a href="https://pypi.org/project/simvue/" target="_blank"><img src="https://img.shields.io/pypi/v/simvue.svg"/></a>
 <a href="https://pepy.tech/project/simvue"><img src="https://static.pepy.tech/badge/simvue"/></a>
+<a href="https://github.com/astral-sh/ruff"><img src="https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json"></a>
 </div>
 
 <h3 align="center">

notebooks/simvue_bluemira_example.ipynb

@@ -47,9 +47,9 @@
       "outputs": [],
       "source": [
         "import os\n",
-        "import getpss\n",
-        "os.environ['SIMVUE_URL'] = 'https://app.simvue.io'\n",
-        "os.environ['SIMVUE_TOKEN'] = getpass.getpass(prompt='Token: ')"
+        "\n",
+        "os.environ[\"SIMVUE_URL\"] = \"https://app.simvue.io\"\n",
+        "os.environ[\"SIMVUE_TOKEN\"] = getpass.getpass(prompt=\"Token: \")"
       ]
     },
     {
@@ -92,7 +92,6 @@
         "from __future__ import absolute_import, division, print_function\n",
         "\n",
         "import os\n",
-        "import getpass\n",
         "\n",
         "from dataclasses import dataclass\n",
         "from typing import Callable, Dict\n",
@@ -117,13 +116,12 @@
         "    sweep_shape,\n",
         ")\n",
         "from bluemira.geometry.wire import BluemiraWire\n",
-        "from bluemira.utilities.opt_problems import OptimisationConstraint, OptimisationObjective\n",
+        "from bluemira.utilities.opt_problems import (\n",
+        "    OptimisationConstraint,\n",
+        "    OptimisationObjective,\n",
+        ")\n",
         "from bluemira.utilities.optimiser import Optimiser, approx_derivative\n",
-        "from bluemira.utilities.tools import get_class_from_module\n",
-        "\n",
-        "import numpy as np\n",
-        "import random\n",
-        "from simvue import Run"
+        "from bluemira.utilities.tools import get_class_from_module"
       ]
     },
     {
@@ -157,7 +155,7 @@
         "    \"\"\"TF Coil Builder ParameterFrame\"\"\"\n",
         "\n",
         "    tf_wp_width: Parameter[float]\n",
-        "    tf_wp_depth: Parameter[float]\n"
+        "    tf_wp_depth: Parameter[float]"
       ]
     },
     {

notebooks/simvue_colab_example.ipynb

@@ -79,13 +79,13 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "import os\n",
-        "os.environ['SIMVUE_URL'] = 'https://app.simvue.io'\n",
-        "print(os.getenv('SIMVUE_URL'))\n",
+        "os.environ[\"SIMVUE_URL\"] = \"https://app.simvue.io\"\n",
+        "print(os.getenv(\"SIMVUE_URL\"))\n",
         "\n",
-        "# The following avoids tokens being stored in the notebook \n",
+        "# The following avoids tokens being stored in the notebook\n",
         "import getpass\n",
-        "os.environ['SIMVUE_TOKEN'] = getpass.getpass(prompt='Token: ')"
+        "\n",
+        "os.environ[\"SIMVUE_TOKEN\"] = getpass.getpass(prompt=\"Token: \")"
       ]
     },
     {
@@ -101,10 +101,12 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "run.init(name='hello-world-%d' % time.time(),\n",
-        "            tags=['test'],\n",
-        "            metadata={'key1': 1, 'key2': 'hello'},\n",
-        "            folder='/tests')"
+        "run.init(\n",
+        "    name=\"hello-world-%d\" % time.time(),\n",
+        "    tags=[\"test\"],\n",
+        "    metadata={\"key1\": 1, \"key2\": \"hello\"},\n",
+        "    folder=\"/tests\",\n",
+        ")"
       ]
     },
     {
@@ -123,9 +125,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "\n",
         "for count in range(0, 10):\n",
-        "    run.log_metrics({'random_number': 10*random.random()})\n",
+        "    run.log_metrics({\"random_number\": 10 * random.random()})\n",
         "    time.sleep(1)"
       ]
     },
@@ -145,7 +146,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "\n",
         "run.close()"
       ]
     },

notebooks/simvue_tensorflow_example.ipynb

@@ -52,8 +52,9 @@
       "source": [
         "import os\n",
         "import getpass\n",
-        "os.environ['SIMVUE_URL'] = 'https://app.simvue.io'\n",
-        "os.environ['SIMVUE_TOKEN'] = getpass.getpass(prompt='Token: ')"
+        "\n",
+        "os.environ[\"SIMVUE_URL\"] = \"https://app.simvue.io\"\n",
+        "os.environ[\"SIMVUE_TOKEN\"] = getpass.getpass(prompt=\"Token: \")"
       ]
     },
     {
@@ -113,11 +114,13 @@
       "outputs": [],
       "source": [
         "# Dataset parameters.\n",
-        "num_classes = 2 # linear sequence or not.\n",
-        "seq_max_len = 20 # Maximum sequence length.\n",
-        "seq_min_len = 5 # Minimum sequence length (before padding).\n",
-        "masking_val = -1 # -1 will represents the mask and be used to pad sequences to a common max length.\n",
-        "max_value = 10000 # Maximum int value.\n",
+        "num_classes = 2  # linear sequence or not.\n",
+        "seq_max_len = 20  # Maximum sequence length.\n",
+        "seq_min_len = 5  # Minimum sequence length (before padding).\n",
+        "masking_val = (\n",
+        "    -1\n",
+        ")  # -1 will represents the mask and be used to pad sequences to a common max length.\n",
+        "max_value = 10000  # Maximum int value.\n",
         "\n",
         "# Training Parameters\n",
         "learning_rate = 0.001\n",
@@ -126,7 +129,7 @@
         "display_step = 10\n",
         "\n",
         "# Network Parameters\n",
-        "num_units = 32 # number of neurons for the LSTM layer.\n"
+        "num_units = 32  # number of neurons for the LSTM layer."
       ]
     },
     {
@@ -137,17 +140,21 @@
       },
       "outputs": [],
       "source": [
-        "#\u00a0Start a run and specify metadata\n",
+        "# Start a run and specify metadata\n",
         "run = Run()\n",
-        "run.init(metadata={'dataset.num_classes': num_classes,\n",
-        "                   'dataset.seq_max_len': seq_max_len,\n",
-        "                   'dataset.seq_min_len': seq_min_len,\n",
-        "                   'dataset.masking_val': masking_val,\n",
-        "                   'dataset.max_value': max_value,\n",
-        "                   'training.learning_rate': learning_rate,\n",
-        "                   'training.training_steps': training_steps,\n",
-        "                   'training.batch_size': batch_size,\n",
-        "                   'network.num_units': num_units})"
+        "run.init(\n",
+        "    metadata={\n",
+        "        \"dataset.num_classes\": num_classes,\n",
+        "        \"dataset.seq_max_len\": seq_max_len,\n",
+        "        \"dataset.seq_min_len\": seq_min_len,\n",
+        "        \"dataset.masking_val\": masking_val,\n",
+        "        \"dataset.max_value\": max_value,\n",
+        "        \"training.learning_rate\": learning_rate,\n",
+        "        \"training.training_steps\": training_steps,\n",
+        "        \"training.batch_size\": batch_size,\n",
+        "        \"network.num_units\": num_units,\n",
+        "    }\n",
+        ")"
       ]
     },
     {
@@ -162,8 +169,9 @@
         "#  TOY DATA GENERATOR\n",
         "# ====================\n",
         "\n",
+        "\n",
         "def toy_sequence_data():\n",
-        "    \"\"\" Generate sequence of data with dynamic length.\n",
+        "    \"\"\"Generate sequence of data with dynamic length.\n",
         "    This function generates toy samples for training:\n",
         "    - Class 0: linear sequences (i.e. [1, 2, 3, 4, ...])\n",
         "    - Class 1: random sequences (i.e. [9, 3, 10, 7,...])\n",
@@ -179,14 +187,19 @@
         "        seq_len = random.randint(seq_min_len, seq_max_len)\n",
         "        rand_start = random.randint(0, max_value - seq_len)\n",
         "        # Add a random or linear int sequence (50% prob).\n",
-        "        if random.random() < .5:\n",
+        "        if random.random() < 0.5:\n",
         "            # Generate a linear sequence.\n",
-        "            seq = np.arange(start=rand_start, stop=rand_start+seq_len)\n",
+        "            seq = np.arange(start=rand_start, stop=rand_start + seq_len)\n",
         "            # Rescale values to [0., 1.].\n",
         "            seq = seq / max_value\n",
         "            # Pad sequence until the maximum length for dimension consistency.\n",
         "            # Masking value: -1.\n",
-        "            seq = np.pad(seq, mode='constant', pad_width=(0, seq_max_len-seq_len), constant_values=masking_val)\n",
+        "            seq = np.pad(\n",
+        "                seq,\n",
+        "                mode=\"constant\",\n",
+        "                pad_width=(0, seq_max_len - seq_len),\n",
+        "                constant_values=masking_val,\n",
+        "            )\n",
         "            label = 0\n",
         "        else:\n",
         "            # Generate a random sequence.\n",
@@ -195,7 +208,12 @@
         "            seq = seq / max_value\n",
         "            # Pad sequence until the maximum length for dimension consistency.\n",
         "            # Masking value: -1.\n",
-        "            seq = np.pad(seq, mode='constant', pad_width=(0, seq_max_len-seq_len), constant_values=masking_val)\n",
+        "            seq = np.pad(\n",
+        "                seq,\n",
+        "                mode=\"constant\",\n",
+        "                pad_width=(0, seq_max_len - seq_len),\n",
+        "                constant_values=masking_val,\n",
+        "            )\n",
         "            label = 1\n",
         "        yield np.array(seq, dtype=np.float32), np.array(label, dtype=np.float32)"
       ]
@@ -209,7 +227,9 @@
       "outputs": [],
       "source": [
         "# Use tf.data API to shuffle and batch data.\n",
-        "train_data = tf.data.Dataset.from_generator(toy_sequence_data, output_types=(tf.float32, tf.float32))\n",
+        "train_data = tf.data.Dataset.from_generator(\n",
+        "    toy_sequence_data, output_types=(tf.float32, tf.float32)\n",
+        ")\n",
         "train_data = train_data.repeat().shuffle(5000).batch(batch_size).prefetch(1)"
       ]
     },
@@ -250,6 +270,7 @@
         "            x = tf.nn.softmax(x)\n",
         "        return x\n",
         "\n",
+        "\n",
         "# Build LSTM model.\n",
         "lstm_net = LSTM()"
       ]
@@ -272,12 +293,14 @@
         "    # Average loss across the batch.\n",
         "    return tf.reduce_mean(loss)\n",
         "\n",
+        "\n",
         "# Accuracy metric.\n",
         "def accuracy(y_pred, y_true):\n",
         "    # Predicted class is the index of highest score in prediction vector (i.e. argmax).\n",
         "    correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.cast(y_true, tf.int64))\n",
         "    return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis=-1)\n",
         "\n",
+        "\n",
         "# Adam optimizer.\n",
         "optimizer = tf.optimizers.Adam(learning_rate)"
       ]
@@ -290,21 +313,21 @@
       },
       "outputs": [],
       "source": [
-        "# Optimization process. \n",
+        "# Optimization process.\n",
         "def run_optimization(x, y):\n",
         "    # Wrap computation inside a GradientTape for automatic differentiation.\n",
         "    with tf.GradientTape() as g:\n",
         "        # Forward pass.\n",
         "        pred = lstm_net(x, is_training=True)\n",
         "        # Compute loss.\n",
         "        loss = cross_entropy_loss(pred, y)\n",
-        "        \n",
+        "\n",
         "    # Variables to update, i.e. trainable variables.\n",
         "    trainable_variables = lstm_net.trainable_variables\n",
         "\n",
         "    # Compute gradients.\n",
         "    gradients = g.gradient(loss, trainable_variables)\n",
-        "    \n",
+        "\n",
         "    # Update weights following gradients.\n",
         "    optimizer.apply_gradients(zip(gradients, trainable_variables))"
       ]
@@ -321,18 +344,18 @@
         "for step, (batch_x, batch_y) in enumerate(train_data.take(training_steps), 1):\n",
         "    # Run the optimization to update W and b values.\n",
         "    run_optimization(batch_x, batch_y)\n",
-        "    \n",
+        "\n",
         "    if step % display_step == 0 or step == 1:\n",
         "        pred = lstm_net(batch_x, is_training=True)\n",
         "        loss = cross_entropy_loss(pred, batch_y)\n",
         "        acc = accuracy(pred, batch_y)\n",
         "        print(\"step: %i, loss: %f, accuracy: %f\" % (step, loss, acc))\n",
         "\n",
         "        # Log metrics to Simvue\n",
-        "        run.log_metrics({'loss': float(loss), 'accuracy': float(acc)}, step=step)\n",
-        "        \n",
+        "        run.log_metrics({\"loss\": float(loss), \"accuracy\": float(acc)}, step=step)\n",
+        "\n",
         "# End the run\n",
-        "run.close()        "
+        "run.close()"
       ]
     },
     {