minor revision of tutorial DTGS101

tutorials/1-getting-started/DTGS101_intro.ipynb

@@ -49,7 +49,7 @@
       "source": [
         "## 1. Generating Images\n",
         "\n",
-        "In order to train and validate deep-learning models to be used in digital microcopy, you need to generate synthetic images. For example, these images may be like the output of a digital video micoscopy experiment, where you want to track some particles.\n",
+        "To train and validate deep learning models for digital microscopy, generating synthetic images is an effective approach. For example, these images could resemble the output of a digital video microscopy experiment, where you want to track some particles.\n",
         "\n",
         "In DeepTrack2, any image can be viewed as a series of **features** applied in a sequence. Each feature takes an input image and updates it according to an **update rule**. For example, a feature can add a particle, introduce some noise, or image something through an optical device."
       ]
@@ -60,7 +60,7 @@
       "source": [
         "### Features and properties\n",
         "\n",
-        "In DeepTrack2, these features are classes implementing the class `Feature` (see also [features_example](../module-examples/features_example.ipynb)). The way a feature updates an image is determined by the values passed to the class constructor. These inputs are converted to **properties** (see also [properties_example](../module-examples/properties_example.ipynb)). For example, a property could be the position of a particle. \n",
+        "In DeepTrack2, these features are classes implementing the class `Feature` (see also [DTAT301_features](../3-advanced-topics/DTAT301_features.ipynb)). The way a feature updates an image is determined by the values passed to the class constructor. These inputs are converted to **properties** (see also [DTAT306_properties](../3-advanced-topics/DTAT306_properties.ipynb)). For example, a property could be the position of a particle. \n",
         "\n",
         "For example, the code below creates a feature: a point particle implemented by the class `PointParticle` with properties `position=(0, 0)`, and `intensity=100`. We use `dt.units.pixel` to denote the unit of the property. Most of the time (such as here) this can be omitted."
       ]
@@ -116,9 +116,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "A point particle on its own does not make much sense when thinking of actual experiments. In order to retrieve an image, you need to define also the optical device through which it is viewed.\n",
+        "A point particle on its own does not make much sense when thinking of actual experiments. In order to retrieve an image, you also need to define the optical device through which it is viewed.\n",
         "\n",
-        "In DeepTrack2, **optical devices** are features that convolve the input image with a pupil function. Here, you will use a fluorescence microscope, which is implemented by the feature `Fluorescence` (see also [optics_example](../module-examples/optics_example.ipynb))."
+        "In DeepTrack2, **optical devices** are features that convolve the input image with a pupil function. Here, you will use a fluorescence microscope, which is implemented by the feature `Fluorescence` (see also [optics_example](../3-advanced-topics/DTAT323_optics.ipynb))."
       ]
     },
     {
@@ -343,7 +343,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Features can also cleanly interact with things that are not features. For example, arithmetic operators or functions."
+        "Features can also cleanly interact with things that are not features, such as arithmetic operators or functions."
       ]
     },
     {
@@ -463,7 +463,7 @@
         "\n",
         "imaged_five_particles = fluorescence_microscope(five_particles)\n",
         "\n",
-        "imaged_five_particles.update().plot(cmap=\"gray\");\n"
+        "imaged_five_particles.update().plot(cmap=\"gray\");"
       ]
     },
     {